diff --git a/8-Reinforcement/1-QLearning/assignment.md b/8-Reinforcement/1-QLearning/assignment.md
index 029c0516..f28f7bad 100644
--- a/8-Reinforcement/1-QLearning/assignment.md
+++ b/8-Reinforcement/1-QLearning/assignment.md
@@ -1,6 +1,6 @@
 # A More Realistic World
 
-In our situation, Peter was able to move around almost without getting tired or hungry. In more realistic world, we has to sit down and rest from time to time, and also to feed himself. Let's make our world more realistic, by implementing the following rules:
+In our situation, Peter was able to move around almost without getting tired or hungry. In a more realistic world, we has to sit down and rest from time to time, and also to feed himself. Let's make our world more realistic, by implementing the following rules:
 
 1. By moving from one place to another, Peter loses **energy** and gains some **fatigue**.
 2. Peter can gain more energy by eating apples.
diff --git a/8-Reinforcement/1-QLearning/notebook.ipynb b/8-Reinforcement/1-QLearning/notebook.ipynb
index 4785bc7e..846ad34a 100644
--- a/8-Reinforcement/1-QLearning/notebook.ipynb
+++ b/8-Reinforcement/1-QLearning/notebook.ipynb
@@ -10,15 +10,15 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.0"
   },
   "orig_nbformat": 2,
   "kernelspec": {
    "name": "python3",
-   "display_name": "Python 3.7.4 64-bit ('base': conda)"
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
   },
   "interpreter": {
-   "hash": "c77bccf6af5544921fca6eddbefe5e7c44ddf71c61b63c74bd828ca1d0e389a0"
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
   }
  },
  "nbformat": 4,
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,10 +64,8 @@
     "\n",
     "To work with the environment, we will define a class called `Board`. In order not to clutter this notebook too much, we have moved all code to work with the board into separate `rlboard` module, which we will now import. You may look inside this module to get more details about the internals of the implementation."
    ],
-   "cell_type": "code",
-   "metadata": {},
-   "execution_count": null,
-   "outputs": []
+   "cell_type": "markdown",
+   "metadata": {}
   },
   {
    "source": [
@@ -78,21 +76,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
+   "outputs": [],
    "source": [
     "# code block 1"
    ]
@@ -101,14 +87,14 @@
    "source": [
     "## Actions and Policy\n",
     "\n",
-    "In our example, Peter's goal would be to find an apple, while avoiding the wolf and other obstacles. To do this, he can essentially walk around until he finds and apple. Therefore, at any position he can chose between one of the following actions: up, down, left and right. We will define those actions as a dictionary, and map them to pairs of corresponding coordinate changes. For example, moving right (`R`) would correspond to a pair `(1,0)`."
+    "In our example, Peter's goal would be to find an apple, while avoiding the wolf and other obstacles. Define those actions as a dictionary, and map them to pairs of corresponding coordinate changes."
    ],
    "cell_type": "markdown",
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -117,13 +103,11 @@
   },
   {
    "source": [
-    "The strategy of our agent (Peter) is defined by a so-called **policy**. A policy is a function that returns the action at any given state. In our case, the state of the problem is represented by the board, including the current position of the player. \n",
-    "\n",
-    "The goal of reinforcement learning is to eventually learn a good policy that will allow us to solve the problem efficiently. However, as a baseline, let's consider the simplest policy called **random walk**.\n",
+    "The strategy of our agent (Peter) is defined by a so-called **policy**. Let's consider the simplest policy called **random walk**.\n",
     "\n",
     "## Random walk\n",
     "\n",
-    "Let's first solve our problem by implementing a random walk strategy. With random walk, we will randomly chose the next action from allowed ones, until we reach the apple. "
+    "Let's first solve our problem by implementing a random walk strategy."
    ],
    "cell_type": "markdown",
    "metadata": {}
@@ -134,22 +118,14 @@
    ],
    "cell_type": "code",
    "metadata": {},
-   "execution_count": null,
+   "execution_count": 6,
    "outputs": []
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Average path length = 32.87096774193548, eaten by wolf: 7 times\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# code block 4"
    ]
@@ -158,7 +134,7 @@
    "source": [
     "## Reward Function\n",
     "\n",
-    "To make our policy more intelligent, we need to understand which moves are \"better\" than others. To do this, we need to define our goal. The goal can be defined in terms of **reward function**, that will return some score value for each state. The higher the number - the better the reward function is.\n",
+    "To make our policy more intelligent, we need to understand which moves are \"better\" than others.\n",
     "\n"
    ],
    "cell_type": "markdown",
@@ -166,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -175,20 +151,16 @@
   },
   {
    "source": [
-    "An interesting thing about the reward function is that in most of the cases *we are only given substantial reward at the end of the game*. It means that our algorithm should somehow remember \"good\" steps that lead to a positive reward at the end and increase their importance. Similarly, all moves that lead to bad results should be discouraged.\n",
-    "\n",
     "## Q-Learning\n",
     "\n",
-    "An algorithm that we will discuss here is called **Q-Learning**. In this algorithm, the policy is defined by a function (or a data structure) called **Q-Table**. It records the \"goodness\" of each of the actions in a given state, i.e. $Q : {S\\times A}\\to\\mathbb{R}$, where $S$ is a set of states, $A$ is the set of actions.\n",
-    "\n",
-    "It is called Q-Table because it is often convenient to represent it as a table, or multi-dimensional array. Since our board has dimentions `width` x `height`, we can represent Q-Table by a numpy array with shape `width` x `height` x `len(actions)`:"
+    "Build a Q-Table, or multi-dimensional array. Since our board has dimentions `width` x `height`, we can represent Q-Table by a numpy array with shape `width` x `height` x `len(actions)`:"
    ],
    "cell_type": "markdown",
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -197,26 +169,26 @@
   },
   {
    "source": [
-    "Notice that we initially initialize all values of Q-Table with equal value, in our case - 0.25. That corresponds to the \"random walk\" policy, because all moves in each state are equally good. We can pass the Q-Table to the `plot` function in order to visualize the table on the board:"
+    "Pass the Q-Table to the `plot` function in order to visualize the table on the board:"
    ],
    "cell_type": "markdown",
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
-     "output_type": "display_data",
-     "data": {
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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
+     "output_type": "error",
+     "ename": "NameError",
+     "evalue": "name 'm' is not defined",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-10-8b8a9e4cb2b8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mQ\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'm' is not defined"
+     ]
     }
    ],
    "source": [
@@ -225,29 +197,10 @@
   },
   {
    "source": [
-    "In the center of each cell there is an \"arrow\" that indicates the preferred direction of movement. Since all directions are equal, a dot is displayed.\n",
-    "\n",
-    "Now we need to run the simulation, explore our environment, and learn better distribution of Q-Table values, which will allow us to find the path to the apple much faster.\n",
-    "\n",
-    "## Essence of Q-Learning: Bellman Equation\n",
-    "\n",
-    "Once we start moving, each action will have a corresponding reward, i.e. we can theoretically select the next action based on the highest immediate reward. However, in most of the states the move will not achieve our goal or reaching the apple, and thus we cannot immediately decide which direction is better.\n",
-    "\n",
-    "> It is not the immediate result that matters, but rather the final result, which we will obtain at the end of the simulation.\n",
-    "\n",
-    "In order to account for this delayed reward, we need to use the principles of **[dynamic programming](https://en.wikipedia.org/wiki/Dynamic_programming)**, which allows us to think about our problem recursively.\n",
-    "\n",
-    "Suppose we are now at the state $s$, and we want to move to the next state $s'$. By doing so, we will receive the immediate reward $r(s,a)$, defined by reward function, plus some future reward. If we suppose that our Q-Table correctly reflects the \"attractiveness\" of each action, then at state $s'$ we will chose an action $a'$ that corresponds to maximum value of $Q(s',a')$. Thus, the best possible future reward we could get at state $s'$ will be defined as $\\max_{a'}Q(s',a')$ (maximum here is computed over all possible actions $a'$ at state $s'$). \n",
-    "\n",
-    "This gives the **Bellman formula** for calculating the value of Q-Table at state $s$, given action $a$:\n",
     "\n",
-    "$$Q(s,a) = r(s,a) + \\gamma \\max_{a'} Q(s',a')$$\n",
+    "## Essence of Q-Learning: Bellman Equation and  Learning Algorithm\n",
     "\n",
-    "Here $\\gamma$ is so-called **discount factor** that determines to which extent you should prefer current reward over the future reward and vice versa.\n",
-    "\n",
-    "## Learning Algorithm\n",
-    "\n",
-    "Given the equation above, we can now write a pseudo-code for our leaning algorithm:\n",
+    "Write a pseudo-code for our leaning algorithm:\n",
     "\n",
     "* Initialize Q-Table Q with equal numbers for all states and actions\n",
     "* Set learning rate $\\alpha\\leftarrow 1$\n",
@@ -264,9 +217,7 @@
     "\n",
     "## Exploit vs. Explore\n",
     "\n",
-    "In the algorithm above, we did not specify how exactly we should chose an action at step 2.1. If we are choosing the action randomly, we will randomly **explore** the environment, and we are quite likely to die often, and also explore such areas where we would not normally go. An alternative approach would be to **exploit** the Q-Table values that we already know, and thus to chose the best action (with highers Q-Table value) at state $s$. This, however, will prevent us from exploring other states, and quite likely we might not find the optimal solution.\n",
-    "\n",
-    "Thus, the best approach is to balance between exploration and exploitation. This can be easily done by choosing the action at state $s$ with probabilities proportional to values in Q-Table. In the beginning, when Q-Table values are all the same, it would correspond to random selection, but as we learn more about our environment, we would be more likely to follow the optimal route, however, choosing the unexplored path once in a while.\n",
+    "The best approach is to balance between exploration and exploitation. As we learn more about our environment, we would be more likely to follow the optimal route, however, choosing the unexplored path once in a while.\n",
     "\n",
     "## Python Implementation\n",
     "\n",
@@ -317,12 +268,10 @@
   },
   {
    "source": [
-    "After executing this algorithm, the Q-Table should be updated with values that define the attractiveness of different actions at each step. We can try to visualize the Q-Table by plotting a vector at each cell that will point in the desired direction of movement. For simplicity, we draw a small circle instead of arrow head."
+    "After executing this algorithm, the Q-Table should be updated with values that define the attractiveness of different actions at each step. Visualize the table here:"
    ],
-   "cell_type": "code",
-   "metadata": {},
-   "execution_count": null,
-   "outputs": []
+   "cell_type": "markdown",
+   "metadata": {}
   },
   {
    "cell_type": "code",
@@ -376,18 +325,14 @@
   },
   {
    "source": [
-    "If you try the code above several times, you may notice that sometimes it just \"hangs\", and you need to press the STOP button in the notebook to interrupt it. This happens because there could be situations when two states \"point\" to each other in terms of optimal Q-Value, in which case the agents ends up moving between those states indefinitely.\n",
+    "If you try the code above several times, you may notice that sometimes it just \"hangs\", and you need to press the STOP button in the notebook to interrupt it. \n",
     "\n",
     "> **Task 1:** Modify the `walk` function to limit the maximum length of path by a certain number of steps (say, 100), and watch the code above return this value from time to time.\n",
     "\n",
-    "> **Task 2:** Modify the `walk` function so that it does not go back to the places where is has already been previously. This will prevent `walk` from looping, however, the agent can still end up being \"trapped\" in a location from which it is unable to escape.\n",
-    "\n",
-    "Better navigation policy would be the one that we have used during training, which combines exploitation and exploration. In this policy, we will select each action with a certain probability, proportional to the values in Q-Table. This strategy may still result in the agent returning back to the position it has already explored, but, as you can see from the code below, it results in very short average path to the desired location (remember that `print_statistics` runs the simulation 100 times):  "
+    "> **Task 2:** Modify the `walk` function so that it does not go back to the places where is has already been previously. This will prevent `walk` from looping, however, the agent can still end up being \"trapped\" in a location from which it is unable to escape."
    ],
-   "cell_type": "code",
-   "metadata": {},
-   "execution_count": null,
-   "outputs": []
+   "cell_type": "markdown",
+   "metadata": {}
   },
   {
    "cell_type": "code",
@@ -409,9 +354,7 @@
   },
   {
    "source": [
-    "## Investigating Learning Process\n",
-    "\n",
-    "As we have mentioned, the learning process is a balance between exploration and exploration of gained knowledge about the structure of problem space. We have seen that the result of learning (the ability to help an agent to find short path to the goal) has improved, but it is also interesting to observe how the average path length behaves during the learning process: "
+    "## Investigating Learning Process"
    ],
    "cell_type": "markdown",
    "metadata": {}
@@ -447,47 +390,15 @@
     "plt.plot(lpath)"
    ]
   },
-  {
-   "source": [
-    "What we see here is that at first the average path length increased. This is probably due to the fact that when we know nothing about the environment - we are likely to get trapped into bad states, water or wolf. As we learn more and start using this knowledge, we can explore the environment for longer, but we still do not know well where apples are.\n",
-    "\n",
-    "Once we learn enough, it becomes easier for the agent to achieve the goal, and the path length starts to decrease. However, we are still open to exploration, so we often diverge away from the best path, and explore new options, making the path longer than optimal.\n",
-    "\n",
-    "What we also observe on this graph, is that at some point the length increased abruptly. This indicates stochastic nature of the process, and that we can at some point \"sploil\" the Q-Table coefficients, by overwriting them with new values. This ideally should be minimized by decreasing learning rate (i.e. towards the end of training we only adjust Q-Table values by a small value).\n",
-    "\n",
-    "Overall, it is important to remember that the success and quality of the learning process significantly depends on parameters, such as leaning rate, learning rate decay and discount factor. Those are often called **hyperparameters**, to distinguish them from **parameters** which we optimize during training (eg. Q-Table coefficients). The process of finding best hyperparameter values is called **hyperparameter optimization**, and it deserves a separate topic."
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
-  },
   {
    "source": [
     "## Exercise\n",
-    "#### More Realistic Peter and the Wolf World\n",
-    "\n",
-    "In our situation, Peter was able to move around almost without getting tired or hungry. In more realistic world, we has to sit down and rest from time to time, and also to feed himself. Let's make our world more realistic, by implementing the following rules:\n",
-    "\n",
-    "1. By moving from one place to another, Peter loses **energy** and gains some **fatigue**.\n",
-    "2. Peter can gain more energy by eating apples.\n",
-    "3. Peter can get rid of fatigue by resting under the tree or on the grass (i.e. walking into a board location with a tree or grass - green field)\n",
-    "4. Peter needs to find and kill the wolf\n",
-    "5. In order to kill the wolf, Peter needs to have certain levels of energy and fatigue, otherwise he loses the battle.\n",
-    "\n",
-    "Modify the reward function above according to the rules of the game, run the reinforcement learning algorithm to learn the best strategy for winning the game, and compare the results of random walk with your algorithm in terms of number of games won and lost.\n",
-    "\n",
+    "## A more realistic Peter and the Wolf world\n",
     "\n",
-    "> **Note**: You may need to adjust hyperparameters to make it work, especially the number of epochs. Because the success of the game (fighting the wolf) is a rare event, you can expect much longer training time.\n",
     "\n"
    ],
    "cell_type": "markdown",
    "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ]
 }
\ No newline at end of file
diff --git a/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb b/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb
index e8b208fa..9c1adae0 100644
--- a/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb
+++ b/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb
@@ -14,12 +14,11 @@
   },
   "orig_nbformat": 2,
   "kernelspec": {
-   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
-   "display_name": "Python 3.7.3 64-bit",
-   "language": "python"
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
   },
   "interpreter": {
-   "hash": "c77bccf6af5544921fca6eddbefe5e7c44ddf71c61b63c74bd828ca1d0e389a0"
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
   }
  },
  "nbformat": 4,
@@ -62,7 +61,7 @@
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\n"
      },
      "metadata": {
@@ -321,7 +320,7 @@
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 792x432 with 1 Axes>",
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      },
      "metadata": {
@@ -381,7 +380,7 @@
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f8128a7ddd8>]"
+       "[<matplotlib.lines.Line2D at 0x7fe0987e7dd8>]"
       ]
      },
      "metadata": {},
@@ -391,7 +390,7 @@
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
      },
      "metadata": {
diff --git a/8-Reinforcement/1-QLearning/solution/notebook.ipynb b/8-Reinforcement/1-QLearning/solution/notebook.ipynb
index 45f9a673..88ba802e 100644
--- a/8-Reinforcement/1-QLearning/solution/notebook.ipynb
+++ b/8-Reinforcement/1-QLearning/solution/notebook.ipynb
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -85,14 +85,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 792x432 with 1 Axes>",
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\n"
      },
      "metadata": {
@@ -118,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -141,7 +141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 16,
    "metadata": {
     "tags": []
    },
@@ -154,7 +154,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 5
+     "execution_count": 16
     }
    ],
    "source": [
@@ -193,7 +193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -231,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -266,7 +266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -282,14 +282,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 792x432 with 1 Axes>",
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\n"
      },
      "metadata": {
@@ -362,7 +362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -383,7 +383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -400,7 +400,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-11-ce307467f9ff>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     28\u001b[0m         \u001b[0mgamma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m         \u001b[0mai\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maction_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m         \u001b[0mQ\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mai\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mQ\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mai\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0malpha\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mgamma\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mQ\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mdpos\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mdpos\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     31\u001b[0m         \u001b[0mn\u001b[0m\u001b[0;34m+=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-22-ce307467f9ff>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     28\u001b[0m         \u001b[0mgamma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m         \u001b[0mai\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maction_idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m         \u001b[0mQ\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mai\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mQ\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mai\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0malpha\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mgamma\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mQ\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mdpos\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mdpos\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     31\u001b[0m         \u001b[0mn\u001b[0m\u001b[0;34m+=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mIndexError\u001b[0m: index 8 is out of bounds for axis 0 with size 8"
      ]
     }
diff --git a/8-Reinforcement/2-Gym/README.md b/8-Reinforcement/2-Gym/README.md
index 21c3269a..a26b234e 100644
--- a/8-Reinforcement/2-Gym/README.md
+++ b/8-Reinforcement/2-Gym/README.md
@@ -1,25 +1,25 @@
 # CartPole Skating
 
-The problem we have been solving in the previous lesson might seem like a toy problem, not really applicable for real life scenarios. This is not the case, because many real world problems are like that - including playing chess or go. They are similar, because we also have a board with given rules and **discrete state**.
+The problem we have been solving in the previous lesson might seem like a toy problem, not really applicable for real life scenarios. This is not the case, because many real world problems are share this scenario - including playing chess or Go. They are similar, because we also have a board with given rules and a **discrete state**.
 
 In this lesson we will apply the same principles of Q-Learning to a problem with **continuous state**, i.e. a state that is given by one or more real numbers. We will deal with the following problem:
 
-> **Problem**: If Peter wants to escape from the wolf, he needs to be able to move faster than him. We will see how Peter can learn to skate, in particular, to keep balance, using Q-Learning.
+> **Problem**: If Peter wants to escape from the wolf, he needs to be able to move faster. We will see how Peter can learn to skate, in particular, to keep balance, using Q-Learning.
 
-We will use a simplified version of balancing known as **CartPole** problem. In cartpole world, we have a horizontal slider that can move left or right, and the goal is to balance a pole staying on top of it.
+We will use a simplified version of balancing known as a **CartPole** problem. In the cartpole world, we have a horizontal slider that can move left or right, and the goal is to balance a vertical pole on top of the slider.
 
-<img src="images/cartpole.png" width="200"/>
+<img alt="a cartpole" src="images/cartpole.png" width="200"/>
 
 ## Prerequisites
 
-In this lesson, we will be using a library called **OpenAI Gym** to simulate different **environments**. It is preferred to run this lesson's code locally (eg. from Visual Studio Code), in which case the simulation will open in a new window. When running the code online, you may need to make some tweaks to the code, as described [here](https://towardsdatascience.com/rendering-openai-gym-envs-on-binder-and-google-colab-536f99391cc7).
+In this lesson, we will be using a library called **OpenAI Gym** to simulate different **environments**. You can run this lesson's code locally (eg. from Visual Studio Code), in which case the simulation will open in a new window. When running the code online, you may need to make some tweaks to the code, as described [here](https://towardsdatascience.com/rendering-openai-gym-envs-on-binder-and-google-colab-536f99391cc7).
 ## OpenAI Gym
 
-In the previous lesson, the rules of the game and the state were given by `Board` class, which we defined ourselves. Here we will use a special **sumulation environment**, which will simulate the physics behind the balancing pole. One of the most popular simulation environments for training Reinforcement Learning algorithms is called [Gym](https://gym.openai.com/), which is maintained by [OpenAI](https://openai.com/). By using gym we can create difference **environments**: from cartpole simulation to Atari games.
+In the previous lesson, the rules of the game and the state were given by the `Board` class which we defined ourselves. Here we will use a special **sumulation environment**, which will simulate the physics behind the balancing pole. One of the most popular simulation environments for training reinforcement learning algorithms is called a [Gym](https://gym.openai.com/), which is maintained by [OpenAI](https://openai.com/). By using this gym we can create difference **environments** from a cartpole simulation to Atari games.
 
 > **Note**: You can see other environments available from OpenAI Gym [here](https://gym.openai.com/envs/#classic_control). 
 
-First, let's install the gym and import required libraries:
+First, let's install the gym and import required libraries (code block 1):
 
 ```python
 import sys
@@ -35,7 +35,7 @@ import random
 
 To work with CartPole balancing problem, we need to initialize corresponding environment. Each environment is associated with:
 * **Observation space** that defines the structure of information that we receive from the environment. For cartpole problem, we receive position of the pole, velocity and some other values.
-* **Action space** that defines possible actions. In our case action space is discrete, and consists of two actions - **left** and **right**.
+* **Action space** that defines possible actions. In our case action space is discrete, and consists of two actions - **left** and **right**. (code block 2)
 
 ```python
 env = gym.make("CartPole-v1")
@@ -46,7 +46,7 @@ print(env.action_space.sample())
 
 To see how the environment works, let's run a short simulation for 100 steps. At each step, we provide one of the actions to be taken - in this simulation we just randomly select an action from `action_space`. Run the code below and see what it leads to.
 
-> **Note**: Remember that it is preferred to run this code on local Python installation!
+> **Note**: Remember that it is preferred to run this code on local Python installation! (code block 3)
 
 ```python
 env.reset()
@@ -59,9 +59,9 @@ env.close()
 
 You should be seeing something similar to this one:
 
-![](images/cartpole-nobalance.gif)
+![non-balancing cartpole](images/cartpole-nobalance.gif)
 
-During simulation, we need to get observatons in order to decide how to act. In fact, `step` function returns us back current observations, reward function, and the `done` flag that indicates whether it makes sense to continue the simulation or not:
+During simulation, we need to get observatons in order to decide how to act. In fact, `step` function returns us back current observations, reward function, and the `done` flag that indicates whether it makes sense to continue the simulation or not: (code block 4)
 
 ```python
 env.reset()
@@ -91,7 +91,7 @@ The observation vector that is returned at each step of the simulation contains
 * Angle of pole
 * Rotation rate of pole
 
-We can get min and max value of those numbers:
+We can get min and max value of those numbers: (code block 5)
 
 ```python
 print(env.observation_space.low)
@@ -113,13 +113,12 @@ There are a few ways we can do this:
 
 In our example, we will go with the second approach. As you may notice later, despite undefined upper/lower bounds, those value rarely take values outside of certain finite intervals, thus those states with extreme values will be very rare.
 
-Here is the function that will take the observation from our model, and produces a tuple of 4 integer values:
-
+Here is the function that will take the observation from our model, and produces a tuple of 4 integer values: (code block 6)
 ```python
 def discretize(x):
     return tuple((x/np.array([0.25, 0.25, 0.01, 0.1])).astype(np.int))
 ```
-Let's also explore other discretization method using bins:
+Let's also explore other discretization method using bins: (code block 7)
 ```python
 def create_bins(i,num):
     return np.arange(num+1)*(i[1]-i[0])/num+i[0]
@@ -136,7 +135,7 @@ def discretize_bins(x):
 
 Let's now run a short simulation and observe those discrete environment values. Feel free to try both `discretize` and `discretize_bins` and see if there is a difference.
 
-> **Note**: `discretize_bins` returns the bin number, which is 0-based, thus for values of input variable around 0 it returns the number from the middle of the interval (10). In `discretize`, we did not care about the range of output values, allowing them to be negative, thus the state values are not shifted, and 0 corresponds to 0.
+> **Note**: `discretize_bins` returns the bin number, which is 0-based, thus for values of input variable around 0 it returns the number from the middle of the interval (10). In `discretize`, we did not care about the range of output values, allowing them to be negative, thus the state values are not shifted, and 0 corresponds to 0. (code block 8)
 
 ```python
 env.reset()
@@ -155,7 +154,7 @@ env.close()
 
 In our previous lesson, the state was a simple pair of numbers from 0 to 8, and thus it was convenient to represent Q-Table by numpy tensor with shape 8x8x2. If we use bins discretization, the size of our state vector is also known, so we can use the same approach and represent state by an array of shape 20x20x10x10x2 (here 2 is the dimension of action space, and first dimensions correspond to the number of bins we have selected to use for each of the parameters in observation space).
 
-However, sometimes precise dimensions of the observation space are not known. In case of `discretize` function, we may never be sure that our state stays within certain limits, because some of the original values are not bound. Thus, we will use slightly different approach and represent Q-Table by a dictionary. We will use the pair *(state,action)* as the dictionary key, and the value would correspond to Q-Table entry value. 
+However, sometimes precise dimensions of the observation space are not known. In case of `discretize` function, we may never be sure that our state stays within certain limits, because some of the original values are not bound. Thus, we will use slightly different approach and represent Q-Table by a dictionary. We will use the pair *(state,action)* as the dictionary key, and the value would correspond to Q-Table entry value. (code block 9)
 
 ```python
 Q = {}
@@ -169,7 +168,7 @@ Here we also define a function `qvalues`, which returns a list of Q-Table values
 
 ## Let's Start Q-Learning!
 
-Now we are ready to teach Peter to balance! First, let's set some hyperparameterers:
+Now we are ready to teach Peter to balance! First, let's set some hyperparameters: (code block 10)
 
 ```python
 # hyperparameters
@@ -191,7 +190,7 @@ We would also make two improvements to our algorithm from the previous lesson:
 * Calculating average cumulative reward over a number of simulations. We will print the progress each 5000 iterations, and we will average out our cumulative reward over that period of time. It means that if we get more than 195 point - we can consider the problem solved, with even higher quality than required.
 * We will calculate maximum average cumulative result `Qmax`, and we will store the Q-Table corresponding to that result. When you run the training you will notice that sometimes the average cumulative result starts to drop, and we want to keep the values of Q-Table that correspond to the best model observed during training.
 
-We will also collect all cumulative rewards at each simulation at `rewards` vector for further plotting.
+We will also collect all cumulative rewards at each simulation at `rewards` vector for further plotting. (code block  11)
 
 ```python
 def probs(v,eps=1e-4):
@@ -242,9 +241,9 @@ This is more clearly visible if we plot training progress.
 
 During training, we have collected the cumulative reward value at each of the iterations into `rewards` vector. Here is how it looks when we plot it against the iteration number:
 
-![](images/train_progress_raw.png)
+![raw  progress](images/train_progress_raw.png)
 
-From this graph, it is not possible to tell anything, because due to the nature of stochastic training process the length of training sessions varies greatly. To make more sense of this graph, we can calculate **running average** over series of experiments, let's say 100. This can be done conveniently using `np.convolve`:
+From this graph, it is not possible to tell anything, because due to the nature of stochastic training process the length of training sessions varies greatly. To make more sense of this graph, we can calculate **running average** over series of experiments, let's say 100. This can be done conveniently using `np.convolve`: (code block 12)
 
 ```python
 def running_average(x,window):
@@ -253,7 +252,7 @@ def running_average(x,window):
 plt.plot(running_average(rewards,100))
 ```
 
-![](images/train_progress_runav.png)
+![training progress](images/train_progress_runav.png)
 
 ## Varying Hyperparameters
 
@@ -267,7 +266,7 @@ To make learning more stable, it makes sense to adjust some of our hyperparamete
 
 ## Seeing the Result in Action
 
-Now it would be interesting to actually see how the trained model behaves. Let's run the simulation, and we will be following the same action selection strategy as during training: sampling according to the probability distribution in Q-Table: 
+Now it would be interesting to actually see how the trained model behaves. Let's run the simulation, and we will be following the same action selection strategy as during training: sampling according to the probability distribution in Q-Table: (code block 13)
 
 ```python
 obs = env.reset()
@@ -283,7 +282,10 @@ env.close()
 
 You should see something like this:
 
-![](images/cartpole-balance.gif)
+![a balancing cartpole](images/cartpole-balance.gif)
+
+---
+## 🚀Challenge
 
 > **Task 3**: Here, we were using the final copy of Q-Table, which may not be the best one. Remember that we have stored the best-performing Q-Table into `Qbest` variable! Try the same example with the best-performing Q-Table by copying `Qbest` over to `Q` and see if you notice the difference.
 
@@ -291,8 +293,10 @@ You should see something like this:
 
 ## [Post-lecture quiz](link-to-quiz-app)
 
-## Assignment: [Train Mountain Car](assignment.md)
+## Assignment: [Train a Mountain Car](assignment.md)
 
 ## Conclusion
 
-We have now learnt how to train agents to achieve good results just by providing them a reward function that defines the desired state of the game, and by giving it an opportunity to intelligently explore the search space. We have successfully applied Q-Learning algorithm in the cases of discrete and continuous environments, but with discrete actions. In the are of reinforcement learning, we need to further study situations where action state is also continuous, and when observation space is much more complex, such as the image from Atari game screen. In those problems we often need to use more powerful machine learning techniques, such as neural networks, in order to achieve good results. Those more advanced topics are the subject of more advanced Deep Reinforcement Learning course.
\ No newline at end of file
+We have now learned how to train agents to achieve good results just by providing them a reward function that defines the desired state of the game, and by giving them an opportunity to intelligently explore the search space. We have successfully applied the Q-Learning algorithm in the cases of discrete and continuous environments, but with discrete actions. 
+
+It's important to also study situations where action state is also continuous, and when observation space is much more complex, such as the image from the Atari game screen. In those problems we often need to use more powerful machine learning techniques, such as neural networks, in order to achieve good results. Those more advanced topics are the subject of our forthcoming more advanced AI course.
\ No newline at end of file
diff --git a/8-Reinforcement/2-Gym/assignment.md b/8-Reinforcement/2-Gym/assignment.md
index 1e755bdb..9bffaa7c 100644
--- a/8-Reinforcement/2-Gym/assignment.md
+++ b/8-Reinforcement/2-Gym/assignment.md
@@ -2,7 +2,7 @@
 
 [OpenAI Gym](http://gym.openai.com) has been designed in such a way that all environments provide the same API - i.e. the same methods `reset`, `step` and `render`, and the same abstractions of **action space** and **observation space**. Thus is should be possible to adapt the same reinforcement learning algorithms to different environments with minimal code changes.
 
-## Mountain Car Environment
+## A Mountain Car Environment
 
 [Mountain Car environment](https://gym.openai.com/envs/MountainCar-v0/) contains a car stuck in a valley:
 
diff --git a/8-Reinforcement/2-Gym/notebook.ipynb b/8-Reinforcement/2-Gym/notebook.ipynb
index 9f421aac..fec01e36 100644
--- a/8-Reinforcement/2-Gym/notebook.ipynb
+++ b/8-Reinforcement/2-Gym/notebook.ipynb
@@ -28,117 +28,53 @@
    "source": [
     "## CartPole Skating\n",
     "\n",
-    "The problem we have been solving in the previous lesson might seem like a toy problem, not really applicable for real life scenarios. This is not the case, because many real world problems are like that - including playing chess or go. They are similar, because we also have a board with given rules and **discrete state**.\n",
-    "\n",
-    "In this lesson we will apply the same principles of Q-Learning to a problem with **continuous state**, i.e. a state that is given by one or more real numbers. We will deal with the following problem:\n",
-    "\n",
     "> **Problem**: If Peter wants to escape from the wolf, he needs to be able to move faster than him. We will see how Peter can learn to skate, in particular, to keep balance, using Q-Learning.\n",
     "\n",
-    "We will use a simplified version of balancing known as **CartPole** problem. In cartpole world, we have a horizontal slider that can move left or right, and the goal is to balance a pole staying on top of it.\n",
-    "\n",
-    "<img src=\"images/cartpole.png\" width=\"200\"/>\n",
-    "\n",
-    "## OpenAI Gym\n",
-    "\n",
-    "In the previous lesson, the rules of the game and the state were given by `Board` class, which we defined ourselves. Here we will use a special **sumulation environment**, which will simulate the physics behind the balancing pole. One of the most popular simulation environments for training Reinforcement Learning algorithms is called [Gym](https://gym.openai.com/), which is maintained by [OpenAI](https://openai.com/). By using gym we can create difference **environments**: from cartpole simulation to Atari games. \n",
-    "\n",
     "First, let's install the gym and import required libraries:"
    ],
    "cell_type": "markdown",
    "metadata": {}
   },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Requirement already satisfied: gym in c:\\winapp\\miniconda3\\lib\\site-packages (0.18.3)\nRequirement already satisfied: pyglet<=1.5.15,>=1.4.0 in c:\\winapp\\miniconda3\\lib\\site-packages (from gym) (1.5.15)\nRequirement already satisfied: cloudpickle<1.7.0,>=1.2.0 in c:\\winapp\\miniconda3\\lib\\site-packages (from gym) (1.2.2)\nRequirement already satisfied: Pillow<=8.2.0 in c:\\winapp\\miniconda3\\lib\\site-packages (from gym) (7.2.0)\nRequirement already satisfied: scipy in c:\\winapp\\miniconda3\\lib\\site-packages (from gym) (1.6.1)\nRequirement already satisfied: numpy>=1.10.4 in c:\\winapp\\miniconda3\\lib\\site-packages (from gym) (1.19.5)\n"
-     ]
-    }
-   ],
-   "source": [
-    "import sys\n",
-    "!{sys.executable} -m pip install gym "
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import gym\n",
-    "import matplotlib.pyplot as plt\n",
-    "from IPython import display\n",
-    "import numpy as np\n",
-    "import random"
+    "#code block 1"
    ]
   },
   {
    "source": [
-    "## CartPole Environment\n",
-    "\n",
-    "To work with CartPole balancing problem, we need to initialize corresponding environment. Each environment is associated with:\n",
-    "* **Observation space** that defines the structure of information that we receive from the environment. For cartpole problem, we receive position of the pole, velocity and some other values.\n",
-    "* **Action space** that defines possible actions. In our case action space is discrete, and consists of two actions - **left** and **right**."
+    "## Create a cartpole environment"
    ],
    "cell_type": "markdown",
    "metadata": {}
   },
   {
+   "source": [
+    "#code block 2"
+   ],
    "cell_type": "code",
-   "execution_count": 2,
    "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Discrete(2)\nBox(-3.4028234663852886e+38, 3.4028234663852886e+38, (4,), float32)\n1\n"
-     ]
-    }
-   ],
-   "source": [
-    "env = gym.make(\"CartPole-v1\")\n",
-    "print(env.action_space)\n",
-    "print(env.observation_space)\n",
-    "print(env.action_space.sample())"
-   ]
+   "execution_count": null,
+   "outputs": []
   },
   {
    "source": [
-    "To see how the environment works, let's run a short simulation for 100 steps. At each step, we provide one of the actions to be taken - in this simulation we just randomly select an action from `action_space`. Run the code below and see what it leads to.\n",
-    "\n",
-    "> **Note**: It is preferred to run this code locally (eg. from Visual Studio Code), in which case the simulation will open in a new window. When running the code online, you may need to make some tweaks to the code, as described [here](https://towardsdatascience.com/rendering-openai-gym-envs-on-binder-and-google-colab-536f99391cc7)."
+    "To see how the environment works, let's run a short simulation for 100 steps."
    ],
    "cell_type": "markdown",
    "metadata": {}
   },
   {
+   "source": [
+    "#code block 3"
+   ],
    "cell_type": "code",
-   "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "C:\\winapp\\miniconda3\\lib\\site-packages\\gym\\logger.py:30: UserWarning: \u001b[33mWARN: You are calling 'step()' even though this environment has already returned done = True. You should always call 'reset()' once you receive 'done = True' -- any further steps are undefined behavior.\u001b[0m\n  warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))\n"
-     ]
-    }
-   ],
-   "source": [
-    "env.reset()\n",
-    "\n",
-    "for i in range(100):\n",
-    "   env.render()\n",
-    "   env.step(env.action_space.sample())\n",
-    "env.close()"
-   ]
+   "execution_count": null,
+   "outputs": []
   },
   {
    "source": [
@@ -148,51 +84,17 @@
    "metadata": {}
   },
   {
+   "source": [
+    "#code block 4"
+   ],
    "cell_type": "code",
-   "execution_count": 4,
    "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "[-0.01781364  0.16446158  0.00575593 -0.26601863] -> 1.0\n",
-      "[-1.45244123e-02  3.59500908e-01  4.35556587e-04 -5.56880543e-01] -> 1.0\n",
-      "[-0.00733439  0.55461674 -0.01070205 -0.84942621] -> 1.0\n",
-      "[ 0.00375794  0.35964236 -0.02769058 -0.56012774] -> 1.0\n",
-      "[ 0.01095079  0.16491978 -0.03889313 -0.27629582] -> 1.0\n",
-      "[ 0.01424918  0.36057441 -0.04441905 -0.58098753] -> 1.0\n",
-      "[ 0.02146067  0.5562897  -0.0560388  -0.8873258 ] -> 1.0\n",
-      "[ 0.03258647  0.75212567 -0.07378532 -1.19708542] -> 1.0\n",
-      "[ 0.04762898  0.55803219 -0.09772702 -0.92841056] -> 1.0\n",
-      "[ 0.05878962  0.75432799 -0.11629524 -1.25013537] -> 1.0\n",
-      "[ 0.07387618  0.56087255 -0.14129794 -0.99602608] -> 1.0\n",
-      "[ 0.08509363  0.75757231 -0.16121846 -1.32953877] -> 1.0\n",
-      "[ 0.10024508  0.56480922 -0.18780924 -1.09133681] -> 1.0\n",
-      "[ 0.11154126  0.76184222 -0.20963598 -1.43658114] -> 1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "env.reset()\n",
-    "\n",
-    "done = False\n",
-    "while not done:\n",
-    "   env.render()\n",
-    "   obs, rew, done, info = env.step(env.action_space.sample())\n",
-    "   print(f\"{obs} -> {rew}\")\n",
-    "env.close()"
-   ]
+   "execution_count": null,
+   "outputs": []
   },
   {
    "source": [
-    "The observation vector that is returned at each step of the simulation contains the following values:\n",
-    "* Position of cart\n",
-    "* Velocity of cart\n",
-    "* Angle of pole\n",
-    "* Rotation rate of pole\n",
-    "\n",
-    "We can get min and max value of those numbers:\n"
+    "We can get min and max value of those numbers:"
    ],
    "cell_type": "markdown",
    "metadata": {}
@@ -211,32 +113,12 @@
     }
    ],
    "source": [
-    "print(env.observation_space.low)\n",
-    "print(env.observation_space.high)"
+    "#code block 5"
    ]
   },
   {
    "source": [
-    "You may also notice that reward value on each simulation step is always 1. This is because our goal is to survive as long as possible, i.e. keep the pole to a reasonably vertical position for the longest period of time.\n",
-    "\n",
-    "> In fact, CartPole simulation is considered solved if we manage to get the average reward of 195 over 100 consecutive trials."
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
-  },
-  {
-   "source": [
-    "## State Discretization\n",
-    "\n",
-    "In Q=Learning, we need to build Q-Table that defines what to do at each state. To be able to do this, we need state to be **discreet**, more precisely, it should contain finite number of disctete values. Thus, we need somehow to **discretize** our observations, mapping them to finite set of states.\n",
-    "\n",
-    "There are a few ways we can do this:\n",
-    "* If we know the interval of a certain value, we can divide this interval into a number of **bins**, and then replace the value by the number of bin that it belongs to. This can be done using numpy [`digitize`](https://numpy.org/doc/stable/reference/generated/numpy.digitize.html) method. In this case, we will precisely know the state size, because it will depend on the number of bins we select for digitalization.\n",
-    "* We can use linear interpolation to bring values to some finite interval (say, from -20 to 20), and then convert numbers to integers by rounding them. This gives us a bit less control on the size of the state, especially if we do not know the exact ranges of input values. For example, in our case 2 out of 4 values do not have upper/lower bounds on their values, which may result in the infinite number of states.\n",
-    "\n",
-    "In our example, we will go with the second approach. As you may notice later, despite undefined upper/lower bounds, those value rarely take values outside of certain finite intervals, thus those states with extreme values will be very rare.\n",
-    "\n",
-    "Here is the function that will take the observation from our model, and produces a tuple of 4 integer values:"
+    "## State Discretization"
    ],
    "cell_type": "markdown",
    "metadata": {}
@@ -247,8 +129,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def discretize(x):\n",
-    "    return tuple((x/np.array([0.25, 0.25, 0.01, 0.1])).astype(np.int))"
+    "#code block 6"
    ]
   },
   {
@@ -272,24 +153,12 @@
     }
    ],
    "source": [
-    "def create_bins(i,num):\n",
-    "    return np.arange(num+1)*(i[1]-i[0])/num+i[0]\n",
-    "\n",
-    "print(\"Sample bins for interval (-5,5) with 10 bins\\n\",create_bins((-5,5),10))\n",
-    "\n",
-    "ints = [(-5,5),(-2,2),(-0.5,0.5),(-2,2)] # intervals of values for each parameter\n",
-    "nbins = [20,20,10,10] # number of bins for each parameter\n",
-    "bins = [create_bins(ints[i],nbins[i]) for i in range(4)]\n",
-    "\n",
-    "def discretize_bins(x):\n",
-    "    return tuple(np.digitize(x[i],bins[i]) for i in range(4))"
+    "#code block 7"
    ]
   },
   {
    "source": [
-    "Let's now run a short simulation and observe those discrete environemnt values. Feel free to try both `discretize` and `discretize_bins` and see if there is a difference.\n",
-    "\n",
-    "> **Note**: `discretize_bins` returns the bin number, which is 0-based, thus for values of input variable around 0 it returns the number from the middle of the interval (10). In `discretize`, we did not care about the range of output values, allowing them to be negative, thus the state values are not shifted, and 0 corresponds to 0."
+    "Let's now run a short simulation and observe those discrete environment values."
    ],
    "cell_type": "markdown",
    "metadata": {}
@@ -308,24 +177,12 @@
     }
    ],
    "source": [
-    "env.reset()\n",
-    "\n",
-    "done = False\n",
-    "while not done:\n",
-    "   #env.render()\n",
-    "   obs, rew, done, info = env.step(env.action_space.sample())\n",
-    "   #print(discretize_bins(obs))\n",
-    "   print(discretize(obs))\n",
-    "env.close()"
+    "#code block 8"
    ]
   },
   {
    "source": [
-    "## Q-Table Structure\n",
-    "\n",
-    "In our previous lesson, the state was a simple pair of numbers from 0 to 8, and thus it was convenient to represent Q-Table by numpy tensor with shape 8x8x2. If we use bins discretization, the size of our state vector is also known, so we can use the same approach and represent state by an array of shape 20x20x10x10x2 (here 2 is the dimension of action space, and first dimensions correspond to the number of bins we have selected to use for each of the parameters in observation space).\n",
-    "\n",
-    "However, sometimes precise dimensions of the observation space are not known. In case of `discretize` function, we may never be sure that our state stays within certain limits, because some of the original values are not bound. Thus, we will use slightly different approach and represent Q-Table by a dictionary. We will use the pair *(state,action)* as the dictionary key, and the value would correspond to Q-Table entry value. "
+    "## Q-Table Structure"
    ],
    "cell_type": "markdown",
    "metadata": {}
@@ -336,20 +193,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "Q = {}\n",
-    "actions = (0,1)\n",
-    "\n",
-    "def qvalues(state):\n",
-    "    return [Q.get((state,a),0) for a in actions]"
+    "#code block 9"
    ]
   },
   {
    "source": [
-    "Here we also define a function `qvalues`, which returns a list of Q-Table values for a given state that correspond to all possible actions. If the entry is not present in the Q-Table, we will return 0 as the default.\n",
-    "\n",
-    "## Let's Start Q-Learning!\n",
-    "\n",
-    "Now we are ready to teach Peter to balance! First, let's set some hyperparameterers:"
+    "## Let's Start Q-Learning!"
    ],
    "cell_type": "markdown",
    "metadata": {}
@@ -360,32 +209,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# hyperparameters\n",
-    "alpha = 0.3\n",
-    "gamma = 0.90\n",
-    "epsilon = 0.9"
+    "#code block 10"
    ]
   },
-  {
-   "source": [
-    "Here, `alpha` is the **learning rate** that defines to which extent we should adjust the current values of Q-Table at each step. In previous lesson we have started with 1, and then decreased `alpha` to lower values during training. In this example we will keep it constant just for simplicity, and you can experiment with adjusting `alpha` values later.\n",
-    "\n",
-    "`gamma` is the **discount factor** that shows to which extent we should prioritize future reward over current reward.\n",
-    "\n",
-    "`epsilon` is the **exploration/exploitation factor** that determines whether we should prefer exploration to exploitation or vice versa. In our algorithm, we will in `epsilon` percent of the cases select the next action according to Q-Table values, and in the remaining number of cases we will execute random action. This will allow us to explore the areas of search space that we have never seen before. \n",
-    "\n",
-    "> In terms of balancing - choosing random action (exploration) would act as a random punch in the wrong direction, and the pole would have to learn how to recover the balance from those \"mistakes\"\n",
-    "\n",
-    "We would also make two improvements to our algorithm from the previous lesson:\n",
-    "\n",
-    "* Calculating average cumulative reward over a number of simulations. We will print the progress each 5000 iterations, and we will average out our cumulative reward over that period of time. It means that if we get more than 195 point - we can consider the problem solved, with even higher quality than required.\n",
-    "* We will calculate maximum average cumulative result `Qmax`, and we will store the Q-Table corresponding to that result. When you run the training, you will notice that sometimes the average cumulative result starts to drop, and we want to keep the values of Q-Table that correspond to the best model observed during training.\n",
-    "\n",
-    "We will also collect all cumulative rewards at each simulation in `rewards` vector for further plotting."
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
-  },
   {
    "cell_type": "code",
    "execution_count": 14,
@@ -419,59 +245,12 @@
     }
    ],
    "source": [
-    "def probs(v,eps=1e-4):\n",
-    "    v = v-v.min()+eps\n",
-    "    v = v/v.sum()\n",
-    "    return v\n",
-    "\n",
-    "random.seed(13)\n",
-    "np.random.seed(13)\n",
-    "env.seed(13)\n",
-    "\n",
-    "Qmax = 0\n",
-    "cum_rewards = []\n",
-    "rewards = []\n",
-    "for epoch in range(100000):\n",
-    "    obs = env.reset()\n",
-    "    done = False\n",
-    "    cum_reward=0\n",
-    "    # == do the simulation ==\n",
-    "    while not done:\n",
-    "        s = discretize(obs)\n",
-    "        if random.random()<epsilon:\n",
-    "            # exploitation - chose the action according to Q-Table probabilities\n",
-    "            v = probs(np.array(qvalues(s)))\n",
-    "            a = random.choices(actions,weights=v)[0]\n",
-    "        else:\n",
-    "            # exploration - randomly chose the action\n",
-    "            a = np.random.randint(env.action_space.n)\n",
-    "\n",
-    "        obs, rew, done, info = env.step(a)\n",
-    "        cum_reward+=rew\n",
-    "        ns = discretize(obs)\n",
-    "        Q[(s,a)] = (1 - alpha) * Q.get((s,a),0) + alpha * (rew + gamma * max(qvalues(ns)))\n",
-    "    cum_rewards.append(cum_reward)\n",
-    "    rewards.append(cum_reward)\n",
-    "    # == Periodically print results and calculate average reward ==\n",
-    "    if epoch%5000==0:\n",
-    "        print(f\"{epoch}: {np.average(cum_rewards)}, alpha={alpha}, epsilon={epsilon}\")\n",
-    "        if np.average(cum_rewards) > Qmax:\n",
-    "            Qmax = np.average(cum_rewards)\n",
-    "            Qbest = Q\n",
-    "        cum_rewards=[]"
+    "#code block 11"
    ]
   },
   {
    "source": [
-    "What you may notice from those results:\n",
-    "* We are very close achieving the goal of getting 195 cumulative reward over 100+ consecutive runs of the simulation, or we may have actually achieved it! Even if we get smaller numbers, we still do not know, because we average over 5000 runs, and only 100 runs is required in the formal criteria.\n",
-    "* Sometimes the reward start to drop, which means that we can \"destroy\" already learnt values in Q-Table with the ones that make situation worse\n",
-    "\n",
-    "This is more clearly visible if we plot training progress.\n",
-    "\n",
-    "## Plotting Training Progress\n",
-    "\n",
-    "During training, we have collected the cumulative reward value at each of the iterations into `rewards` vector. Here is how it looks when we plot it against the iteration number:"
+    "## Plotting Training Progress"
    ],
    "cell_type": "markdown",
    "metadata": {}
@@ -542,25 +321,12 @@
     }
    ],
    "source": [
-    "def running_average(x,window):\n",
-    "    return np.convolve(x,np.ones(window)/window,mode='valid')\n",
-    "\n",
-    "plt.plot(running_average(rewards,100))"
+    "#code block 12"
    ]
   },
   {
    "source": [
-    "## Varying Hyperparameters\n",
-    "\n",
-    "To make learning more stable, it makes sense to adjust some of our hyperparameters during training. In particular:\n",
-    "* For **learning rate**, `alpha`, we may start with values close to 1, and then keep decreasing the parameter. With time, we will be getting good probability values in Q-Table, and thus we should be adjusting them slightly, and not overwriting completely with new values.\n",
-    "* We may want to increase the `eplilon` slowly, in order to be exploring less, and expliting more. It probably makes sense to start with lower value of `epsilon`, and move up to almost 1\n",
-    "\n",
-    "> **Task 1**: Play with hyperparameter values and see if you can achieve higher cumulative reward. Are you getting above 195?\n",
-    "\n",
-    "> **Task 2**: To formally solve the problem, you need to get 195 average reward across 100 consecutive runs. Measure that during training and make sure that you have formally solved the problem!\n",
-    "\n",
-    "## Seeing the Result in Action\n",
+    "## Varying Hyperparameters and Seeing the Result in Action\n",
     "\n",
     "Now it would be interesting to actually see how the trained model behaves. Let's run the simulation, and we will be following the same action selection strategy as during training: sampling according to the probability distribution in Q-Table: "
    ],
@@ -573,24 +339,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "obs = env.reset()\n",
-    "done = False\n",
-    "while not done:\n",
-    "   s = discretize(obs)\n",
-    "   env.render()\n",
-    "   v = probs(np.array(qvalues(s)))\n",
-    "   a = random.choices(actions,weights=v)[0]\n",
-    "   obs,_,done,_ = env.step(a)\n",
-    "env.close()"
+    "# code block 13"
    ]
   },
   {
    "source": [
-    "> **Task 3**: Here, we were using the final copy of Q-Table, which may not be the best one. Remember that we have stored the best-performing Q-Table into `Qbest` variable! Try the same example with the best-performing Q-Table by copying `Qbest` over to `Q` and see if you notice the difference.\n",
     "\n",
-    "> **Task 4**: Here we were not selecting the best action on each step, but rather sampling with corresponding probability distribution. Would it make more sense to always select the best action, with highest Q-Table value? This can be easily done by using `np.argmax` function to find out the action number corresponding to highers Q-Table value. Implement this strategy and see if it improves the balancing.\n",
-    "\n",
-    "## Saving result to animated GIF\n",
+    "## Saving result to an animated GIF\n",
     "\n",
     "If you want to impress your friends, you may want to send them the animated GIF picture of the balancing pole. To do this, we can invoke `env.render` to produce an image frame, and then save those to animated GIF using PIL library:"
    ],
@@ -628,22 +383,6 @@
     "ims[0].save('images/cartpole-balance.gif',save_all=True,append_images=ims[1::2],loop=0,duration=5)\n",
     "print(i)"
    ]
-  },
-  {
-   "source": [
-    "## Conclusion\n",
-    "\n",
-    "We have now learnt how to train agents to achieve good results just by providing them a reward function that defines the desired state of the game, and by giving it an opportunity to intelligently explore the search space. We have successfully applied Q-Learning algorithm in the cases of discrete and continuous environments, but with discrete actions. In the are of reinforcement learning, we need to further study situations where action state is also continuous, and when observation space is much more complex, such as the image from Atari game screen. In those problems we often need to use more powerful machine learning techniques, such as neural networks, in order to achieve good results. Those more advanced topics are the subject of more advanced Deep Reinforcement Learning course."
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ]
 }
\ No newline at end of file
diff --git a/8-Reinforcement/2-Gym/solution/notebook.ipynb b/8-Reinforcement/2-Gym/solution/notebook.ipynb
new file mode 100644
index 00000000..d7f5297a
--- /dev/null
+++ b/8-Reinforcement/2-Gym/solution/notebook.ipynb
@@ -0,0 +1,539 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "## CartPole Skating\n",
+    "\n",
+    "> **Problem**: If Peter wants to escape from the wolf, he needs to be able to move faster than him. We will see how Peter can learn to skate, in particular, to keep balance, using Q-Learning.\n",
+    "\n",
+    "First, let's install the gym and import required libraries:"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Collecting gym\n",
+      "  Downloading gym-0.18.3.tar.gz (1.6 MB)\n",
+      "\u001b[K     |████████████████████████████████| 1.6 MB 2.3 MB/s \n",
+      "\u001b[?25hRequirement already satisfied: scipy in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.4.1)\n",
+      "Requirement already satisfied: numpy>=1.10.4 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.19.2)\n",
+      "Collecting pyglet<=1.5.15,>=1.4.0\n",
+      "  Downloading pyglet-1.5.15-py3-none-any.whl (1.1 MB)\n",
+      "\u001b[K     |████████████████████████████████| 1.1 MB 3.7 MB/s \n",
+      "\u001b[?25hRequirement already satisfied: Pillow<=8.2.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (7.0.0)\n",
+      "Collecting cloudpickle<1.7.0,>=1.2.0\n",
+      "  Downloading cloudpickle-1.6.0-py3-none-any.whl (23 kB)\n",
+      "Building wheels for collected packages: gym\n",
+      "  Building wheel for gym (setup.py) ... \u001b[?25ldone\n",
+      "\u001b[?25h  Created wheel for gym: filename=gym-0.18.3-py3-none-any.whl size=1657514 sha256=578c789ab75e603e58dd1152b2bd60d9a5adc6a057559cf8b5bdd6ee8b80abf2\n",
+      "  Stored in directory: /Users/jenlooper/Library/Caches/pip/wheels/1a/ec/6d/705d53925f481ab70fd48ec7728558745eeae14dfda3b49c99\n",
+      "Successfully built gym\n",
+      "Installing collected packages: pyglet, cloudpickle, gym\n",
+      "Successfully installed cloudpickle-1.6.0 gym-0.18.3 pyglet-1.5.15\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "!{sys.executable} -m pip install gym \n",
+    "\n",
+    "import gym\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random"
+   ]
+  },
+  {
+   "source": [
+    "## Create a cartpole environment"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "env = gym.make(\"CartPole-v1\")\n",
+    "print(env.action_space)\n",
+    "print(env.observation_space)\n",
+    "print(env.action_space.sample())"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 2,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Discrete(2)\nBox(-3.4028234663852886e+38, 3.4028234663852886e+38, (4,), float32)\n1\n"
+     ]
+    }
+   ]
+  },
+  {
+   "source": [
+    "To see how the environment works, let's run a short simulation for 100 steps."
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "for i in range(100):\n",
+    "   env.render()\n",
+    "   env.step(env.action_space.sample())\n",
+    "env.close()"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 3,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/gym/logger.py:30: UserWarning: \u001b[33mWARN: You are calling 'step()' even though this environment has already returned done = True. You should always call 'reset()' once you receive 'done = True' -- any further steps are undefined behavior.\u001b[0m\n  warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))\n"
+     ]
+    }
+   ]
+  },
+  {
+   "source": [
+    "During simulation, we need to get observations in order to decide how to act. In fact, `step` function returns us back current observations, reward function, and the `done` flag that indicates whether it makes sense to continue the simulation or not:"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "done = False\n",
+    "while not done:\n",
+    "   env.render()\n",
+    "   obs, rew, done, info = env.step(env.action_space.sample())\n",
+    "   print(f\"{obs} -> {rew}\")\n",
+    "env.close()"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 4,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[-0.035025    0.21201857 -0.010404   -0.3300738 ] -> 1.0\n",
+      "[-0.03078463  0.40728707 -0.01700547 -0.62601941] -> 1.0\n",
+      "[-0.02263889  0.21240657 -0.02952586 -0.3387403 ] -> 1.0\n",
+      "[-0.01839076  0.01771693 -0.03630067 -0.05551247] -> 1.0\n",
+      "[-0.01803642  0.21334007 -0.03741092 -0.35942391] -> 1.0\n",
+      "[-0.01376962  0.40897331 -0.0445994  -0.66366469] -> 1.0\n",
+      "[-0.00559015  0.21449925 -0.05787269 -0.38535156] -> 1.0\n",
+      "[-0.00130017  0.410393   -0.06557972 -0.69570532] -> 1.0\n",
+      "[ 0.00690769  0.21623893 -0.07949383 -0.42436686] -> 1.0\n",
+      "[ 0.01123247  0.02232776 -0.08798116 -0.15776523] -> 1.0\n",
+      "[ 0.01167903  0.21859198 -0.09113647 -0.47685598] -> 1.0\n",
+      "[ 0.01605087  0.02486705 -0.10067359 -0.21423159] -> 1.0\n",
+      "[ 0.01654821  0.22127341 -0.10495822 -0.53689749] -> 1.0\n",
+      "[ 0.02097368  0.02777162 -0.11569617 -0.27904318] -> 1.0\n",
+      "[ 0.02152911 -0.16552613 -0.12127703 -0.02497378] -> 1.0\n",
+      "[ 0.01821859 -0.35871897 -0.12177651  0.22711886] -> 1.0\n",
+      "[ 0.01104421 -0.16208621 -0.11723413 -0.10135989] -> 1.0\n",
+      "[ 0.00780248 -0.35534992 -0.11926133  0.15215788] -> 1.0\n",
+      "[ 0.00069548 -0.15874009 -0.11621817 -0.17564179] -> 1.0\n",
+      "[-0.00247932  0.03783674 -0.11973101 -0.50260923] -> 1.0\n",
+      "[-0.00172258  0.23442478 -0.12978319 -0.83049704] -> 1.0\n",
+      "[ 0.00296591  0.04129289 -0.14639313 -0.58128481] -> 1.0\n",
+      "[ 0.00379177  0.23812991 -0.15801883 -0.9162682 ] -> 1.0\n",
+      "[ 0.00855437  0.04545686 -0.17634419 -0.67712384] -> 1.0\n",
+      "[ 0.00946351  0.24253346 -0.18988667 -1.01973114] -> 1.0\n",
+      "[ 0.01431417  0.05037919 -0.21028129 -0.7921723 ] -> 1.0\n"
+     ]
+    }
+   ]
+  },
+  {
+   "source": [
+    "We can get min and max value of those numbers:"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]\n[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(env.observation_space.low)\n",
+    "print(env.observation_space.high)"
+   ]
+  },
+  {
+   "source": [
+    "## State Discretization"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def discretize(x):\n",
+    "    return tuple((x/np.array([0.25, 0.25, 0.01, 0.1])).astype(np.int))"
+   ]
+  },
+  {
+   "source": [
+    "Let's also explore other discretization method using bins:"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Sample bins for interval (-5,5) with 10 bins\n [-5. -4. -3. -2. -1.  0.  1.  2.  3.  4.  5.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def create_bins(i,num):\n",
+    "    return np.arange(num+1)*(i[1]-i[0])/num+i[0]\n",
+    "\n",
+    "print(\"Sample bins for interval (-5,5) with 10 bins\\n\",create_bins((-5,5),10))\n",
+    "\n",
+    "ints = [(-5,5),(-2,2),(-0.5,0.5),(-2,2)] # intervals of values for each parameter\n",
+    "nbins = [20,20,10,10] # number of bins for each parameter\n",
+    "bins = [create_bins(ints[i],nbins[i]) for i in range(4)]\n",
+    "\n",
+    "def discretize_bins(x):\n",
+    "    return tuple(np.digitize(x[i],bins[i]) for i in range(4))"
+   ]
+  },
+  {
+   "source": [
+    "Let's now run a short simulation and observe those discrete environment values."
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(0, 0, -1, -3)\n(0, 0, -2, 0)\n(0, 0, -2, -3)\n(0, 1, -3, -6)\n(0, 0, -4, -3)\n(0, 1, -5, -6)\n(0, 2, -6, -9)\n(0, 1, -8, -6)\n(0, 2, -9, -9)\n(0, 2, -11, -13)\n(0, 2, -14, -10)\n(0, 1, -16, -8)\n(0, 2, -18, -11)\n(0, 3, -20, -15)\n(0, 2, -23, -12)\n"
+     ]
+    }
+   ],
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "done = False\n",
+    "while not done:\n",
+    "   #env.render()\n",
+    "   obs, rew, done, info = env.step(env.action_space.sample())\n",
+    "   #print(discretize_bins(obs))\n",
+    "   print(discretize(obs))\n",
+    "env.close()"
+   ]
+  },
+  {
+   "source": [
+    "## Q-Table Structure"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Q = {}\n",
+    "actions = (0,1)\n",
+    "\n",
+    "def qvalues(state):\n",
+    "    return [Q.get((state,a),0) for a in actions]"
+   ]
+  },
+  {
+   "source": [
+    "## Let's Start Q-Learning!"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# hyperparameters\n",
+    "alpha = 0.3\n",
+    "gamma = 0.9\n",
+    "epsilon = 0.90"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "0: 108.0, alpha=0.3, epsilon=0.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "def probs(v,eps=1e-4):\n",
+    "    v = v-v.min()+eps\n",
+    "    v = v/v.sum()\n",
+    "    return v\n",
+    "\n",
+    "Qmax = 0\n",
+    "cum_rewards = []\n",
+    "rewards = []\n",
+    "for epoch in range(100000):\n",
+    "    obs = env.reset()\n",
+    "    done = False\n",
+    "    cum_reward=0\n",
+    "    # == do the simulation ==\n",
+    "    while not done:\n",
+    "        s = discretize(obs)\n",
+    "        if random.random()<epsilon:\n",
+    "            # exploitation - chose the action according to Q-Table probabilities\n",
+    "            v = probs(np.array(qvalues(s)))\n",
+    "            a = random.choices(actions,weights=v)[0]\n",
+    "        else:\n",
+    "            # exploration - randomly chose the action\n",
+    "            a = np.random.randint(env.action_space.n)\n",
+    "\n",
+    "        obs, rew, done, info = env.step(a)\n",
+    "        cum_reward+=rew\n",
+    "        ns = discretize(obs)\n",
+    "        Q[(s,a)] = (1 - alpha) * Q.get((s,a),0) + alpha * (rew + gamma * max(qvalues(ns)))\n",
+    "    cum_rewards.append(cum_reward)\n",
+    "    rewards.append(cum_reward)\n",
+    "    # == Periodically print results and calculate average reward ==\n",
+    "    if epoch%5000==0:\n",
+    "        print(f\"{epoch}: {np.average(cum_rewards)}, alpha={alpha}, epsilon={epsilon}\")\n",
+    "        if np.average(cum_rewards) > Qmax:\n",
+    "            Qmax = np.average(cum_rewards)\n",
+    "            Qbest = Q\n",
+    "        cum_rewards=[]"
+   ]
+  },
+  {
+   "source": [
+    "## Plotting Training Progress"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814bb79788>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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190.649933 \r\nL 110.042537 102.25969 \r\nL 110.112542 169.35592 \r\nL 110.212983 195.872993 \r\nL 110.139935 103.465011 \r\nL 110.216026 145.651264 \r\nL 110.243419 95.831308 \r\nL 110.270812 211.542173 \r\nL 110.325598 112.705809 \r\nL 110.34386 197.881863 \r\nL 110.352991 107.080976 \r\nL 110.43517 127.973215 \r\nL 110.471694 116.723548 \r\nL 110.444301 191.453481 \r\nL 110.474738 148.061907 \r\nL 110.483869 212.345721 \r\nL 110.523437 66.903592 \r\nL 110.58431 125.562572 \r\nL 110.684751 208.327982 \r\nL 110.599528 113.911131 \r\nL 110.699969 168.552373 \r\nL 110.769974 93.822439 \r\nL 110.727362 196.274767 \r\nL 110.812585 133.999823 \r\nL 110.897808 212.345721 \r\nL 110.843022 55.653925 \r\nL 110.922157 129.982084 \r\nL 110.967812 109.893392 \r\nL 110.961725 176.989623 \r\nL 110.998249 119.937738 \r\nL 111.001292 209.13153 \r\nL 111.049991 109.491619 \r\nL 111.107821 112.705809 \r\nL 111.202174 206.319113 \r\nL 111.217393 162.927539 \r\nL 111.235655 100.25082 \r\nL 111.250873 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190.649933 \r\nL 113.743636 158.508027 \r\nL 113.801466 196.274767 \r\nL 113.758854 49.627317 \r\nL 113.850164 150.874324 \r\nL 113.862339 94.224213 \r\nL 113.898863 210.336851 \r\nL 113.956693 115.116452 \r\nL 113.984086 194.667672 \r\nL 114.002348 86.188736 \r\nL 114.069308 161.320443 \r\nL 114.102789 199.087184 \r\nL 114.139313 70.117783 \r\nL 114.163662 134.401596 \r\nL 114.215404 82.170998 \r\nL 114.251928 203.90847 \r\nL 114.273234 158.106253 \r\nL 114.32802 109.089845 \r\nL 114.358457 195.47122 \r\nL 114.382806 133.196275 \r\nL 114.422374 198.68541 \r\nL 114.449767 113.509357 \r\nL 114.486291 127.973215 \r\nL 114.56847 17.083636 \r\nL 114.538033 202.301375 \r\nL 114.592819 111.098714 \r\nL 114.620212 213.551042 \r\nL 114.611081 59.269889 \r\nL 114.702391 180.605588 \r\nL 114.711522 82.572772 \r\nL 114.708478 203.90847 \r\nL 114.815007 132.794501 \r\nL 114.827181 191.051707 \r\nL 114.903273 56.859246 \r\nL 114.921535 177.793171 \r\nL 114.982408 70.519557 \r\nL 114.930666 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121.611515 180.203814 \r\nL 121.614558 68.108914 \r\nL 121.635864 80.563902 \r\nL 121.745436 203.90847 \r\nL 121.644995 74.537295 \r\nL 121.751523 181.810909 \r\nL 121.763698 44.404257 \r\nL 121.842833 190.24816 \r\nL 121.861095 138.419335 \r\nL 121.903707 206.319113 \r\nL 121.918925 34.359911 \r\nL 121.955449 107.482749 \r\nL 121.958493 37.975876 \r\nL 121.991973 208.729756 \r\nL 122.061977 119.535965 \r\nL 122.098501 185.0251 \r\nL 122.138069 28.735078 \r\nL 122.171549 153.284967 \r\nL 122.275034 76.14439 \r\nL 122.177637 195.069446 \r\nL 122.281121 150.070776 \r\nL 122.372431 95.831308 \r\nL 122.293296 201.497827 \r\nL 122.393737 98.241951 \r\nL 122.463741 206.720887 \r\nL 122.427217 86.992284 \r\nL 122.503309 94.224213 \r\nL 122.515484 198.283636 \r\nL 122.57027 44.002483 \r\nL 122.615925 167.748825 \r\nL 122.701147 69.314235 \r\nL 122.722453 194.667672 \r\nL 122.731584 77.751486 \r\nL 122.768108 199.890732 \r\nL 122.780283 54.448603 \r\nL 122.847243 161.722217 \r\nL 122.880724 50.430865 \r\nL 122.853331 187.837517 \r\nL 122.956815 72.528426 \r\nL 122.962903 205.917339 \r\nL 123.02682 64.492949 \r\nL 123.069431 175.784302 \r\nL 123.121173 73.733747 \r\nL 123.154654 209.935078 \r\nL 123.179003 150.47255 \r\nL 123.18509 200.694279 \r\nL 123.200309 60.073437 \r\nL 123.282488 151.677871 \r\nL 123.285531 57.26102 \r\nL 123.361623 207.524435 \r\nL 123.395103 101.054368 \r\nL 123.489457 197.480089 \r\nL 123.422496 71.323104 \r\nL 123.504675 163.329313 \r\nL 123.547287 90.206475 \r\nL 123.586854 193.060576 \r\nL 123.614247 133.999823 \r\nL 123.647728 75.742616 \r\nL 123.723819 203.104922 \r\nL 123.763387 81.36745 \r\nL 123.836435 114.714678 \r\nL 123.897308 197.480089 \r\nL 123.942963 17.083636 \r\nL 124.028186 198.68541 \r\nL 124.052535 136.410466 \r\nL 124.152976 65.296497 \r\nL 124.107321 200.292506 \r\nL 124.165151 95.429534 \r\nL 124.171238 110.69694 \r\nL 124.177326 78.956807 \r\nL 124.232112 211.140399 \r\nL 124.286898 166.543503 \r\nL 124.393426 94.625987 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205.917339 \r\nL 127.13577 107.080976 \r\nL 127.150988 168.954146 \r\nL 127.199687 74.135521 \r\nL 127.248386 141.231752 \r\nL 127.26056 193.864124 \r\nL 127.312303 88.599379 \r\nL 127.354914 184.623326 \r\nL 127.370132 44.806031 \r\nL 127.418831 195.872993 \r\nL 127.464486 155.69561 \r\nL 127.50101 210.336851 \r\nL 127.546665 41.59184 \r\nL 127.567971 136.410466 \r\nL 127.589276 62.082306 \r\nL 127.59232 204.310244 \r\nL 127.677543 115.518226 \r\nL 127.695805 77.349712 \r\nL 127.74146 185.0251 \r\nL 127.759722 17.083636 \r\nL 127.851032 68.510687 \r\nL 127.878425 199.087184 \r\nL 127.887556 17.083636 \r\nL 127.963647 145.24949 \r\nL 128.061045 41.59184 \r\nL 128.064088 212.747494 \r\nL 128.073219 42.395388 \r\nL 128.109743 184.623326 \r\nL 128.1067 17.083636 \r\nL 128.185835 143.240621 \r\nL 128.201053 89.001153 \r\nL 128.252796 195.47122 \r\nL 128.295407 111.098714 \r\nL 128.3228 189.042838 \r\nL 128.383673 41.190067 \r\nL 128.404979 152.481419 \r\nL 128.462809 32.351042 \r\nL 128.423241 194.265898 \r\nL 128.514551 142.035299 \r\nL 128.56325 208.729756 \r\nL 128.566293 51.234412 \r\nL 128.624123 150.47255 \r\nL 128.730651 46.011353 \r\nL 128.715433 208.327982 \r\nL 128.736739 103.465011 \r\nL 128.751957 172.971885 \r\nL 128.74587 80.162129 \r\nL 128.803699 140.428204 \r\nL 128.858485 33.958137 \r\nL 128.864573 204.712018 \r\nL 128.913271 151.276098 \r\nL 128.919359 192.658803 \r\nL 128.998494 63.287627 \r\nL 129.016756 110.295166 \r\nL 129.077629 60.876984 \r\nL 129.025887 186.230421 \r\nL 129.114153 117.928869 \r\nL 129.117197 166.945277 \r\nL 129.175027 50.832639 \r\nL 129.223725 161.320443 \r\nL 129.321123 63.287627 \r\nL 129.308948 199.890732 \r\nL 129.333297 84.983415 \r\nL 129.357647 198.283636 \r\nL 129.409389 53.243282 \r\nL 129.442869 145.24949 \r\nL 129.518961 20.297827 \r\nL 129.491568 197.078315 \r\nL 129.549398 161.722217 \r\nL 129.640708 202.301375 \r\nL 129.610271 33.556364 \r\nL 129.655926 179.80204 \r\nL 129.716799 19.092506 \r\nL 129.668101 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132.498711 84.179867 \r\nL 132.568715 196.274767 \r\nL 132.516973 70.519557 \r\nL 132.61437 176.186075 \r\nL 132.626545 80.162129 \r\nL 132.653938 203.506696 \r\nL 132.733073 105.875654 \r\nL 132.775685 207.926208 \r\nL 132.800034 66.903592 \r\nL 132.842645 109.089845 \r\nL 132.882213 194.265898 \r\nL 132.906562 74.939069 \r\nL 132.943086 139.222882 \r\nL 132.955261 48.020222 \r\nL 133.013091 185.426874 \r\nL 133.052658 84.581641 \r\nL 133.156143 212.747494 \r\nL 133.089182 34.359911 \r\nL 133.165274 159.311574 \r\nL 133.174405 85.786962 \r\nL 133.250497 191.855255 \r\nL 133.268759 170.159468 \r\nL 133.271802 211.943947 \r\nL 133.350938 54.046829 \r\nL 133.375287 117.125322 \r\nL 133.472684 45.609579 \r\nL 133.387462 212.345721 \r\nL 133.484859 105.875654 \r\nL 133.490946 188.23929 \r\nL 133.548776 17.083636 \r\nL 133.594431 131.58918 \r\nL 133.679654 19.092506 \r\nL 133.664435 192.658803 \r\nL 133.700959 170.963016 \r\nL 133.704003 201.497827 \r\nL 133.761833 56.859246 \r\nL 133.810531 173.775432 \r\nL 133.868361 52.439734 \r\nL 133.898798 185.0251 \r\nL 133.920103 89.402927 \r\nL 133.971846 204.712018 \r\nL 133.993151 76.546164 \r\nL 134.032719 195.872993 \r\nL 134.111854 32.351042 \r\nL 134.148378 114.312905 \r\nL 134.157509 60.475211 \r\nL 134.203164 184.221552 \r\nL 134.242732 97.840177 \r\nL 134.321867 203.104922 \r\nL 134.267081 54.046829 \r\nL 134.352304 198.283636 \r\nL 134.382741 17.887184 \r\nL 134.46492 135.205144 \r\nL 134.53188 31.145721 \r\nL 134.486225 194.667672 \r\nL 134.574492 113.911131 \r\nL 134.592754 199.488958 \r\nL 134.662758 33.15459 \r\nL 134.68102 115.92 \r\nL 134.684064 25.922661 \r\nL 134.735806 184.623326 \r\nL 134.787548 82.974545 \r\nL 134.790592 204.310244 \r\nL 134.842334 65.698271 \r\nL 134.89712 125.964346 \r\nL 134.95495 43.60071 \r\nL 134.991474 203.104922 \r\nL 135.009736 79.760355 \r\nL 135.098002 203.506696 \r\nL 135.085828 50.029091 \r\nL 135.119308 113.107583 \r\nL 135.204531 46.011353 \r\nL 135.201487 158.9098 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32.351042 \r\nL 136.601574 148.061907 \r\nL 136.616792 17.083636 \r\nL 136.653316 211.140399 \r\nL 136.711146 138.017561 \r\nL 136.772019 188.641064 \r\nL 136.74767 64.492949 \r\nL 136.817674 136.008692 \r\nL 136.90594 69.716009 \r\nL 136.881591 182.212683 \r\nL 136.924202 111.500488 \r\nL 136.978988 195.47122 \r\nL 137.009425 56.055698 \r\nL 137.033774 76.546164 \r\nL 137.097691 51.234412 \r\nL 137.106822 195.47122 \r\nL 137.152477 17.083636 \r\nL 137.219438 93.822439 \r\nL 137.2377 206.319113 \r\nL 137.286399 22.306696 \r\nL 137.325966 68.912461 \r\nL 137.395971 17.887184 \r\nL 137.347272 191.453481 \r\nL 137.435538 42.797162 \r\nL 137.459888 193.060576 \r\nL 137.548154 87.795831 \r\nL 137.599896 66.903592 \r\nL 137.581634 211.542173 \r\nL 137.621202 141.633525 \r\nL 137.721643 209.935078 \r\nL 137.691206 17.083636 \r\nL 137.72773 114.312905 \r\nL 137.809909 193.46235 \r\nL 137.840346 66.501818 \r\nL 137.898176 205.515565 \r\nL 137.952962 166.141729 \r\nL 138.004704 29.940399 \r\nL 137.974267 202.301375 \r\nL 138.065577 105.072106 \r\nL 138.141669 199.488958 \r\nL 138.102101 73.733747 \r\nL 138.147756 113.911131 \r\nL 138.196455 17.083636 \r\nL 138.214717 207.926208 \r\nL 138.257328 56.055698 \r\nL 138.327333 200.694279 \r\nL 138.269503 17.083636 \r\nL 138.3669 191.855255 \r\nL 138.369944 71.724878 \r\nL 138.439948 197.480089 \r\nL 138.479516 107.884523 \r\nL 138.48256 190.649933 \r\nL 138.500822 45.609579 \r\nL 138.592132 148.865455 \r\nL 138.598219 22.306696 \r\nL 138.601263 210.738625 \r\nL 138.704747 119.535965 \r\nL 138.723009 210.336851 \r\nL 138.786926 91.81357 \r\nL 138.814319 165.739956 \r\nL 138.875193 80.162129 \r\nL 138.844756 194.265898 \r\nL 138.923891 154.892062 \r\nL 138.987808 213.149268 \r\nL 138.993896 75.742616 \r\nL 139.036507 191.453481 \r\nL 139.060856 71.323104 \r\nL 139.146079 183.418004 \r\nL 139.173472 67.305366 \r\nL 139.152166 196.274767 \r\nL 139.276957 143.642395 \r\nL 139.359136 195.069446 \r\nL 139.286088 25.520887 \r\nL 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163.440628 100.652594 \r\nL 163.468021 203.90847 \r\nL 163.519763 56.055698 \r\nL 163.5502 197.881863 \r\nL 163.574549 17.083636 \r\nL 163.604986 203.90847 \r\nL 163.662815 71.724878 \r\nL 163.690208 206.720887 \r\nL 163.684121 17.083636 \r\nL 163.772387 103.866785 \r\nL 163.854566 32.752816 \r\nL 163.805868 199.488958 \r\nL 163.878916 133.196275 \r\nL 163.979357 195.872993 \r\nL 163.976313 30.743947 \r\nL 163.988488 113.509357 \r\nL 164.09806 193.46235 \r\nL 164.031099 17.083636 \r\nL 164.101103 135.606918 \r\nL 164.201544 31.145721 \r\nL 164.113278 207.926208 \r\nL 164.210675 71.323104 \r\nL 164.31416 207.122661 \r\nL 164.216763 18.690732 \r\nL 164.323291 140.428204 \r\nL 164.390252 21.904922 \r\nL 164.344597 204.310244 \r\nL 164.423732 126.36612 \r\nL 164.499824 198.283636 \r\nL 164.472431 34.761685 \r\nL 164.533304 129.178537 \r\nL 164.606352 201.899601 \r\nL 164.591134 47.216674 \r\nL 164.609396 197.078315 \r\nL 164.694618 18.690732 \r\nL 164.655051 206.319113 \r\nL 164.718968 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168.764001 39.984745 \r\nL 168.861398 144.044169 \r\nL 168.940533 35.163459 \r\nL 168.964883 184.221552 \r\nL 168.97097 88.599379 \r\nL 169.037931 199.087184 \r\nL 169.010538 41.190067 \r\nL 169.083586 150.874324 \r\nL 169.092717 48.421996 \r\nL 169.174896 193.46235 \r\nL 169.199245 86.59051 \r\nL 169.284468 196.676541 \r\nL 169.2449 56.055698 \r\nL 169.308817 91.81357 \r\nL 169.366647 63.689401 \r\nL 169.397083 197.881863 \r\nL 169.409258 78.153259 \r\nL 169.506655 191.855255 \r\nL 169.439695 23.913792 \r\nL 169.521874 136.008692 \r\nL 169.534048 30.743947 \r\nL 169.57666 207.524435 \r\nL 169.634489 36.36878 \r\nL 169.710581 194.265898 \r\nL 169.646664 17.083636 \r\nL 169.747105 191.051707 \r\nL 169.847546 27.529756 \r\nL 169.862764 50.430865 \r\nL 169.877983 195.47122 \r\nL 169.957118 17.083636 \r\nL 169.972336 129.982084 \r\nL 170.027122 17.083636 \r\nL 170.036253 193.864124 \r\nL 170.084952 73.331973 \r\nL 170.151913 207.926208 \r\nL 170.121476 17.083636 \r\nL 170.194524 141.231752 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196.676541 \r\nL 176.921028 98.241951 \r\nL 177.009294 208.327982 \r\nL 176.933202 17.083636 \r\nL 177.027556 137.214013 \r\nL 177.033643 17.083636 \r\nL 177.036687 208.327982 \r\nL 177.137128 99.447273 \r\nL 177.191914 212.747494 \r\nL 177.167565 34.761685 \r\nL 177.249744 183.819778 \r\nL 177.350185 17.083636 \r\nL 177.316704 199.890732 \r\nL 177.362359 86.59051 \r\nL 177.417145 207.122661 \r\nL 177.411058 37.975876 \r\nL 177.444538 119.134191 \r\nL 177.535848 17.083636 \r\nL 177.481062 203.506696 \r\nL 177.55411 83.778093 \r\nL 177.602809 201.096053 \r\nL 177.566285 17.083636 \r\nL 177.663682 152.883193 \r\nL 177.691075 26.324435 \r\nL 177.748905 201.899601 \r\nL 177.776298 133.999823 \r\nL 177.79456 61.278758 \r\nL 177.800647 201.096053 \r\nL 177.873695 125.160798 \r\nL 177.876739 209.13153 \r\nL 177.971093 41.59184 \r\nL 177.986311 184.623326 \r\nL 178.065447 17.083636 \r\nL 178.016748 205.113792 \r\nL 178.095883 83.376319 \r\nL 178.114145 192.257029 \r\nL 178.168931 17.083636 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205.515565 \r\nL 179.581193 41.190067 \r\nL 179.599455 197.078315 \r\nL 179.608586 37.172328 \r\nL 179.693808 189.444612 \r\nL 179.699896 17.083636 \r\nL 179.775987 205.113792 \r\nL 179.80338 92.617118 \r\nL 179.903821 207.122661 \r\nL 179.845992 17.083636 \r\nL 179.912952 198.283636 \r\nL 179.992088 33.15459 \r\nL 179.943389 202.703149 \r\nL 180.022524 42.395388 \r\nL 180.083398 202.301375 \r\nL 180.049917 17.083636 \r\nL 180.13514 99.045499 \r\nL 180.211232 193.060576 \r\nL 180.162533 21.904922 \r\nL 180.22645 189.042838 \r\nL 180.28428 17.083636 \r\nL 180.244712 193.864124 \r\nL 180.339066 94.224213 \r\nL 180.418201 178.194945 \r\nL 180.402983 17.083636 \r\nL 180.451681 123.553703 \r\nL 180.527773 197.480089 \r\nL 180.53386 37.975876 \r\nL 180.576472 17.083636 \r\nL 180.564297 196.676541 \r\nL 180.628214 73.331973 \r\nL 180.661694 201.096053 \r\nL 180.734742 19.494279 \r\nL 180.74083 191.855255 \r\nL 180.819965 39.984745 \r\nL 180.853445 113.107583 \r\nL 180.862576 190.24816 \r\nL 180.868664 19.494279 \r\nL 180.953886 105.072106 \r\nL 181.039109 17.083636 \r\nL 180.999541 191.855255 \r\nL 181.063458 70.92133 \r\nL 181.130419 188.641064 \r\nL 181.090851 17.083636 \r\nL 181.17303 70.117783 \r\nL 181.218685 17.083636 \r\nL 181.206511 199.087184 \r\nL 181.276515 149.267228 \r\nL 181.309995 37.975876 \r\nL 181.376956 199.087184 \r\nL 181.486528 17.083636 \r\nL 181.404349 199.488958 \r\nL 181.489572 28.735078 \r\nL 181.556532 199.488958 \r\nL 181.498703 17.083636 \r\nL 181.602187 153.284967 \r\nL 181.638711 24.315565 \r\nL 181.635668 213.551042 \r\nL 181.708716 78.555033 \r\nL 181.769589 199.890732 \r\nL 181.733065 41.190067 \r\nL 181.821331 163.731086 \r\nL 181.912641 17.083636 \r\nL 181.833506 211.542173 \r\nL 181.933947 89.804701 \r\nL 181.958296 37.574102 \r\nL 181.949165 168.954146 \r\nL 181.970471 52.03796 \r\nL 182.061781 196.676541 \r\nL 182.064825 17.083636 \r\nL 182.080043 97.438404 \r\nL 182.128742 17.083636 \r\nL 182.168309 197.480089 \r\nL 182.186571 149.267228 \r\nL 182.26875 196.274767 \r\nL 182.238314 17.083636 \r\nL 182.271794 119.937738 \r\nL 182.366148 192.658803 \r\nL 182.378322 17.083636 \r\nL 182.411803 205.515565 \r\nL 182.487894 172.971885 \r\nL 182.515287 17.083636 \r\nL 182.497025 211.140399 \r\nL 182.60051 90.206475 \r\nL 182.688776 191.855255 \r\nL 182.703995 28.333304 \r\nL 182.707038 88.599379 \r\nL 182.798348 193.060576 \r\nL 182.810523 17.083636 \r\nL 182.850091 203.506696 \r\nL 182.920095 83.778093 \r\nL 182.938357 179.400266 \r\nL 183.026623 17.083636 \r\nL 183.124021 192.257029 \r\nL 183.136195 162.123991 \r\nL 183.163588 19.896053 \r\nL 183.236636 194.667672 \r\nL 183.245767 31.547494 \r\nL 183.27316 19.494279 \r\nL 183.358383 198.68541 \r\nL 183.431431 17.083636 \r\nL 183.467955 173.775432 \r\nL 183.541003 195.47122 \r\nL 183.489261 19.494279 \r\nL 183.556221 179.80204 \r\nL 183.632313 17.083636 \r\nL 183.589702 203.90847 \r\nL 183.668837 103.465011 \r\nL 183.723623 212.747494 \r\nL 183.738841 21.904922 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185.315461 191.453481 \r\nL 185.382421 198.283636 \r\nL 185.428076 17.083636 \r\nL 185.528518 201.497827 \r\nL 185.540692 155.293836 \r\nL 185.558954 193.060576 \r\nL 185.647221 17.083636 \r\nL 185.732443 211.943947 \r\nL 185.759836 119.937738 \r\nL 185.814622 195.47122 \r\nL 185.848103 17.083636 \r\nL 185.869408 127.571441 \r\nL 185.930282 17.083636 \r\nL 185.884627 179.400266 \r\nL 185.969849 52.841508 \r\nL 185.972893 196.274767 \r\nL 185.97898 17.083636 \r\nL 186.079421 141.633525 \r\nL 186.182906 17.083636 \r\nL 186.170731 196.676541 \r\nL 186.192037 31.547494 \r\nL 186.246823 197.480089 \r\nL 186.204212 17.083636 \r\nL 186.304653 138.821109 \r\nL 186.362482 213.149268 \r\nL 186.417268 17.083636 \r\nL 186.469011 202.703149 \r\nL 186.52684 50.430865 \r\nL 186.545102 199.087184 \r\nL 186.566408 21.503149 \r\nL 186.657718 171.364789 \r\nL 186.752072 17.083636 \r\nL 186.666849 206.319113 \r\nL 186.76729 95.027761 \r\nL 186.855556 183.819778 \r\nL 186.797727 17.083636 \r\nL 186.873818 80.965676 \r\nL 186.980347 17.083636 \r\nL 186.965128 209.533304 \r\nL 186.98339 25.119113 \r\nL 187.089919 196.676541 \r\nL 187.050351 17.083636 \r\nL 187.092962 156.900931 \r\nL 187.129486 17.083636 \r\nL 187.19036 194.667672 \r\nL 187.202534 74.939069 \r\nL 187.254277 205.917339 \r\nL 187.211665 17.083636 \r\nL 187.31515 120.339512 \r\nL 187.327325 211.943947 \r\nL 187.333412 17.083636 \r\nL 187.424722 178.596718 \r\nL 187.500814 17.083636 \r\nL 187.433853 209.935078 \r\nL 187.540381 42.395388 \r\nL 187.613429 198.68541 \r\nL 187.607342 17.083636 \r\nL 187.656041 172.971885 \r\nL 187.662128 17.083636 \r\nL 187.710827 188.23929 \r\nL 187.768656 73.331973 \r\nL 187.7717 189.846386 \r\nL 187.853879 17.083636 \r\nL 187.878228 57.26102 \r\nL 187.957364 17.083636 \r\nL 187.936058 203.90847 \r\nL 187.978669 93.420665 \r\nL 188.039543 199.087184 \r\nL 188.009106 17.083636 \r\nL 188.091285 166.945277 \r\nL 188.133896 195.872993 \r\nL 188.118678 17.083636 \r\nL 188.152158 78.956807 \r\nL 188.155202 17.083636 \r\nL 188.200857 196.676541 \r\nL 188.255643 188.641064 \r\nL 188.368259 17.083636 \r\nL 188.301298 209.935078 \r\nL 188.380433 20.297827 \r\nL 188.471743 189.444612 \r\nL 188.413914 17.083636 \r\nL 188.490005 88.599379 \r\nL 188.584359 17.083636 \r\nL 188.496093 198.283636 \r\nL 188.59349 167.347051 \r\nL 188.602621 189.444612 \r\nL 188.611752 103.465011 \r\nL 188.614796 17.083636 \r\nL 188.715237 199.890732 \r\nL 188.721324 58.868115 \r\nL 188.785241 192.658803 \r\nL 188.782197 42.797162 \r\nL 188.830896 120.339512 \r\nL 188.83394 25.119113 \r\nL 188.910031 194.265898 \r\nL 188.937424 188.641064 \r\nL 188.943512 190.24816 \r\nL 188.949599 153.686741 \r\nL 189.007429 74.135521 \r\nL 189.034822 198.283636 \r\nL 189.05004 124.759024 \r\nL 189.08352 197.881863 \r\nL 189.120044 17.083636 \r\nL 189.159612 155.293836 \r\nL 189.171787 17.083636 \r\nL 189.202223 193.060576 \r\nL 189.269184 156.900931 \r\nL 189.272228 192.257029 \r\nL 189.360494 49.627317 \r\nL 189.372669 91.81357 \r\nL 189.47311 17.083636 \r\nL 189.409193 213.952816 \r\nL 189.479197 119.535965 \r\nL 189.518765 198.283636 \r\nL 189.530939 30.743947 \r\nL 189.549201 146.454812 \r\nL 189.652686 17.083636 \r\nL 189.576594 191.051707 \r\nL 189.658773 186.632195 \r\nL 189.683123 17.083636 \r\nL 189.774433 63.287627 \r\nL 189.874874 212.747494 \r\nL 189.810957 17.083636 \r\nL 189.884005 86.59051 \r\nL 189.899223 17.083636 \r\nL 189.908354 197.078315 \r\nL 189.944878 185.0251 \r\nL 189.947922 195.872993 \r\nL 189.969227 17.083636 \r\nL 190.030101 136.812239 \r\nL 190.121411 17.083636 \r\nL 190.094018 205.515565 \r\nL 190.139673 154.892062 \r\nL 190.252288 17.083636 \r\nL 190.227939 211.140399 \r\nL 190.258376 52.439734 \r\nL 190.279681 191.855255 \r\nL 190.27055 17.083636 \r\nL 190.374035 170.159468 \r\nL 190.377079 174.177206 \r\nL 190.380122 54.046829 \r\nL 190.413603 17.083636 \r\nL 190.422734 191.453481 \r\nL 190.486651 50.029091 \r\nL 190.532306 202.703149 \r\nL 190.501869 17.083636 \r\nL 190.60231 187.435743 \r\nL 190.644921 26.726208 \r\nL 190.711882 169.35592 \r\nL 190.7271 19.896053 \r\nL 190.809279 190.24816 \r\nL 190.827541 91.411796 \r\nL 190.885371 17.887184 \r\nL 190.873196 205.917339 \r\nL 190.894502 157.704479 \r\nL 190.90972 190.649933 \r\nL 190.961463 27.127982 \r\nL 190.964506 119.535965 \r\nL 191.067991 17.083636 \r\nL 190.979725 201.497827 \r\nL 191.077122 31.547494 \r\nL 191.089297 17.083636 \r\nL 191.095384 157.302705 \r\nL 191.125821 89.804701 \r\nL 191.147126 185.0251 \r\nL 191.162345 17.083636 \r\nL 191.235393 78.956807 \r\nL 191.293222 17.083636 \r\nL 191.311484 210.738625 \r\nL 191.344965 26.726208 \r\nL 191.36627 186.230421 \r\nL 191.454537 22.306696 \r\nL 191.45758 89.001153 \r\nL 191.54889 17.083636 \r\nL 191.494104 206.319113 \r\nL 191.564109 160.115122 \r\nL 191.570196 198.68541 \r\nL 191.673681 17.083636 \r\nL 191.682812 89.804701 \r\nL 191.701074 202.703149 \r\nL 191.69803 17.083636 \r\nL 191.792384 109.089845 \r\nL 191.834995 17.083636 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320.128594 80.965676 \r\nL 320.162075 22.306696 \r\nL 320.204686 185.0251 \r\nL 320.235123 116.321774 \r\nL 320.320345 194.667672 \r\nL 320.286865 17.083636 \r\nL 320.338607 38.779424 \r\nL 320.402524 17.083636 \r\nL 320.387306 184.221552 \r\nL 320.432961 155.69561 \r\nL 320.51514 50.430865 \r\nL 320.442092 205.515565 \r\nL 320.554708 56.055698 \r\nL 320.652105 196.274767 \r\nL 320.566882 17.083636 \r\nL 320.66428 80.162129 \r\nL 320.691673 17.083636 \r\nL 320.703847 188.23929 \r\nL 320.75559 124.759024 \r\nL 320.840812 202.301375 \r\nL 320.776895 17.083636 \r\nL 320.859074 93.018891 \r\nL 320.959515 17.083636 \r\nL 320.941253 188.23929 \r\nL 320.97169 53.243282 \r\nL 321.047782 191.453481 \r\nL 321.063 17.083636 \r\nL 321.081262 67.305366 \r\nL 321.09648 188.641064 \r\nL 321.178659 17.083636 \r\nL 321.184747 71.323104 \r\nL 321.2791 198.68541 \r\nL 321.291275 17.083636 \r\nL 321.330843 183.418004 \r\nL 321.403891 92.617118 \r\nL 321.483026 17.083636 \r\nL 321.501288 176.989623 \r\nL 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rIiL1FclZNwY8Aixzzt0V9tBcYLqfng48G1Z+gT/7Zjiwo3SIR0REGl9aBHVGAT8ElphZ6SD4DcAc4AkzmwF8BUzzj70ATAFWAXuBi6La4gho6EZEpFydQe+ce5uarxM2sZr6DrjsMNslIiJRom/GiogEXCCDXl+YEhEpF8igFxGRcgp6EZGAC2TQry7aHe8miIgkjEAG/fKvd8W7CSIiCSOQQS8iIuUU9CIiAaegFxEJOAW9iEjAKehFRAJOQS8iEnAKehGRgFPQi4gEnIJeRCTgFPQiIgGnoBcRCTgFvYhIwCnoRUQCTkEvIhJwCnoRkYBT0IuIBFxggr5w2954N0FEJCEFIujfXf0No+94Ld7NEBFJSIEI+uVf74x3E0REElYggt65eLdARCRxBSPo490AEZEEFoigFxGRmgUi6J3GbkREahSIoBcRkZo1+aDfd/AQtz6/LN7NEBFJWHUGvZn9zsw2m9nSsLKOZjbPzFb63x18uZnZvWa2ysw+MbPBsWw8wINvfBHrRYiINGmR9Oj/AEyuVDYbmO+cywPm+3mAU4E8/zMTuD86zazZ3oPFsV6EiEiTVmfQO+feBLZWKp4KPOqnHwXODCv/owt5D2hvZlnRamz1DYzpq4uINHkNHaPv6pzbCOB/d/Hl2cC6sHqFvqwKM5tpZgvNbGFRUVEDmwH/WLyhwc8VEUkG0T4Ya9WUVdvnds495JzLd87lZ2ZmNniBG3bsa/BzRUSSQUODflPpkIz/vdmXFwLdw+rlAOpyi4jEUUODfi4w3U9PB54NK7/An30zHNhROsQjIiLxkVZXBTP7CzAO6GxmhcDPgDnAE2Y2A/gKmOarvwBMAVYBe4GLYtBmERGphzqD3jl3bg0PTaymrgMuO9xGiYhI9DT5b8aKiEjtFPQiIgHXpIP+/TWVv8clIiKVNemg/8HDC+LdBBGRhNekg/7AoZJ4N0FEJOE16aAXEZG6KehFRAJOQS8iEnAKehGRgFPQi4gEnIJeRCTgFPQiInHUPC32MaygFxGJo7OH5MR8GQp6EZE4Om9Yj5gvQ0EvIhJHA7LbxXwZCnoRkTjp3aV1oyxHQS8iEidj8zIbZTkKehGROLnmlD6NshwFvYhInLRsXufdXKNCQS8iEnAKeklIfbo2zkEqaTzpjfDFIKme/vKSkJyLdwsk2sbkdY53E5KWgl6kEfzyO8dGXPfYRjivuql69OJhUX29Fs1So/p63Tu2iOrrRYuCXqSBxveNzalxd04bGPUAasq+Oyi7bPrEPuV/87F9av/7FxyXVedr5+d2qHd7Tu7ftcJ8r8xWjIvReyFaFPRS5vwTYv9V7Ibo27VNteV/nnFClbJOrZrHtC3nDO1eNn36wG4AXDDiSNbOKaj1ec1SrV7LuXh0br3bVpe1cwq45uTGOZ0vmobkdqBV86offJUDd3CP9hXmczrU3btOT0vh0nFHlQ0rRXKBsXF9u1SYP2twDtntQ8tqlpqYkZqYrZKo6pXZKqJ6t9VjeKHyP1ldLh/fO+K6o3tXHMt9+eqxVercd94gRsdhzLe095bZJp0zBnbjigm9+ckpfWt9zohenfhOWK80El3bZgBUG3CljvB1EtWpA4447Nd49rJRnDesBwtunMTim08GKgb43y4Zwc2n9WfBDRN57N+H07LS36tTq+ZMOrrm9+pPTunHdZNDPwBnDc7m/RsncvGonmV1hlbq9We2Secfl48um+/SJp3Zp/bjigm9Oe3YmvcirpjQm/wj678HEQ0K+iZkUKUeS6T+3/mDK7xxo2FafndunHI0AMNyO9ZZ/5pKYTiqd6da6885K/Sh819Tj6n28dOO6xZJMyOWF/ZV9PvOG8S8aj5cSv3homF8cOMk0lJTmHVyX9pkNAPgwpG5NK+mR/eXmcNJS03hhin9uHBkbll5Zpt0js5qW+0y+vi9mP+aOqDC3kL3ji147ZpxADzxHyPKyksD5KcFR5eVfS+/6lURv5ffvcK6Ho7OrdOrLX/1xyfy4pVjKqxruJqWX10IDuzeHjOjdXoa7VqG/s5XTepT9jpDczty8eiedG2bQUazVF798ThOCuuELLrpJB6enl82f+e0gfz7mPL/hTYZofPYB2S345Hp+fzs9GPo0iajQs/+4QuGcsvp/cvmT+rflWNz2vHFL6fwwA+GcPaQHNpkNGPWyX0Z2L3m/9ExfTI55ZjyD7+nLh1RY91oU9AniLp2Gbu0SeepS0Y26LWz2rXg5tP7c1xO+UG+W88cUOtzurWrvbc4rm8mk32P7ZJxvSJqx2XjjyqbPmdo7cNEQ47syNo5BVwwIheAD26cVGub75w2kGd+NLLsHzcSFjaaMm/WiRSE9cbyahguqs0tZxzD57edWuPjM8cexS1nlH9wDc3twItXjikbAgo3vFcn3r5uPGf5S9iWhvbLV42lZ+dWrJ1TQI9OLcvq//b8wVxzch9mjC4Psdu+cyzvXT+xwut2aZvBvFkncvNp/XllVs0fZvURvmdxZKeW9MpsXeMHWG2evDSy9/dZg7N569rxnNCramfhiHYZDO5R9QOjTXoak47uwtlDcrixoD9PXjKCn5zSl27ty/cOJh7dlQx/bCR8CLBdy2ZcOKon78yewJrbp5SVp6QYkwccgYW9kSaG7T3MOqkPr10zjldmjeXqSX3IP7JD2V7oHy4aypAj6+4gRYuCPgZKd+XrMx561/cG1vr4+zdOIiXFuOec4+vVlrVzCmjXItQTmnv5aOZ8N9RT7ndE7UFWenbDhSNzeSSsRwSw5vYpNEtNoXvHlqydU8CEflV3jR/4wZAqZT85pR9r5xSwdk5BhXBb/ovJQGg4qH9WW2af2q/KczPbpJeFeAffswt39pAcBvXoUHYlwNJeY/hxh/Drfg/s3p41txfwyqyxvHjlmBr+CiGDe7RniO9tRnMM9r/PDm3z/zl3EGvnFPC3S0YwMKcduZ1DAZ7ToTzIf3X2QNbOKajxm5Rd22Zw+YQ8zKys09AsNYUj2mUw7+qxvHRVxXW8eHRPenWuX8++8tlAE/t1oW/XNtz2ndAH8IhenXiyls5Ih5ah8ByTl1n2Plg7p4B7zjm+wvBKm/TaP6zNjO4dW9b4+FmDs8nr0pofDj+yrGzJz0/h4elDy+bzcztyWS3DiWP6hAK5Z+fyYc/s9i0qhHpNXrpqDPOuHssVE/Po2bkVvbu04cpJoW1zdFZb1s4pqDLOH2uN8/3bAMjr0pqnfjSS4275Z511Z4zuya59xfzbmF7c+c/PAUhLMYpLKp4cfnL/rvTLasuQIzsw6qjqhzLeu34i7cOC7YyB3fh80y4y0lJplpbCWYNzuP2FZTz90XpG9e7ElRP70KVNOu+s3sJx2VV3I78/tDsjj+pMj04tueOsY8nr2oY9+4tZun4nEOoZp1ioR/v2dePJateC1JSKb+7q3uxzLx/FkvU7uPGZpQBMHnAE/bPa8tnGndUeNAX4xZkDGNS9PRnNUnn3+gl0apVe655NwbFZLP96F5ecWL5n8OAPh1QYLik9KHbd5H4s/HIbV07MY8/+Yv7+8QbunDaQJxcVAnD/+YMB6N2l5g+8966fSIdWzUhPS2XvgWLunb+K8yI4YD0mrzOL123nzWvHY1T9Ww3IbsvufcW0qhRoQ3M78mzY2G8k/jRjGDu/La5Q9tx/juatlVvK5mvaOyndjOP6ZnJS/65l2y6rXQYbd+yjR8eWfLV1LwA/P+MYpo/MJXf287RJT2PX/mLyurbmjrOPA+Cta8eT3b4FKWHvlaP8h+2IXp1494tv6N+tLW9PGl/l2MLU47OZenzoGMaHN51Es1TjpLveJDuCg6nVKd1rORyl262+B9EB+h1R/72ZWDOXAN9Myc/PdwsXLqz380bcPp+NO/bV+3nXTu5L6/Q0bn72U1o0S+XpH40kNcW455WVPL9kIytvO5UHXl/Nr+eFQvrm0/pzsd8lds5x07NLuXBkLt978D227jnA9/JzSE1J4cqJeXz5zZ4Ku5TTf/c+Zw7qxstLN/HSp1+z+pdTqgRnuE8Kt3PGfe9w7rDuXDAiN6Jd4H0HD/H6is1MHlD36WSH465/rmDX/mJ+dnr14+YAS9fvoEXzVI7KbM3WPQf4dMMOxjTSFfoADhSXMH/Zpiq71KWeWlTIc59s4PcXVT0fe8XXu7jssQ956tKRZXtBySR39vMAvH/DRFYV7eaYrHZ8+NU29hwoZsqArLIQd87xwpKvmTzgiFrfy6VKShwvLo28fiLYX3yIs+7/Fz8t6M/waoaIEoWZLXLO5ddZLxZBb2aTgXuAVOBh59yc2uo3NOiLD5XwzEfr2VdcQtuMNIbmdqR5WgqpZlz+lw+57cxjSU0x9heXRHTd5/3Fh9iy+wDZ7VtQUuJYt20vR3aK7IyVSF77m90HKowJ1uTLb/bQo2PLiHYTRaJlz/5i9uwvpkuCn80j5eIW9GaWCnwOnAQUAh8A5zrnPqvpOQ0NehGRZBZp0MfiYOwwYJVz7gvn3AHgr8DUGCxHREQiEIugzwbWhc0X+rIKzGymmS00s4VFRUUxaIaIiEBsgr66geUq40POuYecc/nOufzMzMS+ToSISFMWi6AvBLqHzecAG2KwHBERiUAsgv4DIM/MeppZc+AcYG4MliMiIhGI+hemnHPFZnY58DKh0yt/55z7NNrLERGRyMTkm7HOuReAF2Lx2iIiUj+61o2ISMAlxCUQzKwI+LKBT+8MbKmzVrBonZOD1jk5HM46H+mcq/O0xYQI+sNhZgsj+WZYkGidk4PWOTk0xjpr6EZEJOAU9CIiAReEoH8o3g2IA61zctA6J4eYr3OTH6MXEZHaBaFHLyIitVDQi4gEXJMOejObbGYrzGyVmc2Od3vqw8y6m9lrZrbMzD41syt9eUczm2dmK/3vDr7czOxev66fmNngsNea7uuvNLPpYeVDzGyJf869liC3rDKzVDP7yMye8/M9zWyBb//j/hpJmFm6n1/lH88Ne43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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(rewards)"
+   ]
+  },
+  {
+   "source": [
+    "From this graph, it is not possible to tell anything, because due to the nature of stochastic training process the length of training sessions varies greatly. To make more sense of this graph, we can calculate **running average** over series of experiments, let's say 100. This can be done conveniently using `np.convolve`:"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814cc63488>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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110.421698 \r\nL 116.97379 100.967522 \r\nL 116.979883 101.257861 \r\nL 117.092611 107.246111 \r\nL 117.120031 106.3388 \r\nL 117.135264 104.941542 \r\nL 117.184011 116.434143 \r\nL 117.187058 116.440191 \r\nL 117.260178 117.625744 \r\nL 117.244945 114.298938 \r\nL 117.287598 115.678051 \r\nL 117.336345 105.95773 \r\nL 117.403372 110.669696 \r\nL 117.406419 110.669696 \r\nL 117.500866 98.608515 \r\nL 117.421652 111.812908 \r\nL 117.58008 100.30821 \r\nL 117.613593 105.570611 \r\nL 117.66234 97.441108 \r\nL 117.677574 97.586278 \r\nL 117.756787 86.80138 \r\nL 117.829908 93.442893 \r\nL 117.988335 105.752073 \r\nL 117.991382 103.889062 \r\nL 118.076689 113.905771 \r\nL 118.088876 110.488234 \r\nL 118.119343 112.61739 \r\nL 118.161996 108.54659 \r\nL 118.180276 108.570784 \r\nL 118.183323 108.334884 \r\nL 118.229023 113.137581 \r\nL 118.253397 115.901854 \r\nL 118.28691 109.919653 \r\nL 118.326517 111.982272 \r\nL 118.442291 101.83854 \r\nL 118.366124 113.542846 \r\nL 118.460571 103.495894 \r\nL 118.463618 103.876965 \r\nL 118.524552 97.997592 \r\nL 118.673839 87.884104 \r\nL 118.536738 98.348419 \r\nL 118.698213 90.708864 \r\nL 118.85664 106.768261 \r\nL 118.859687 106.683579 \r\nL 118.899294 103.949549 \r\nL 118.932807 110.960036 \r\nL 118.966321 120.595674 \r\nL 119.051628 113.433969 \r\nL 119.094281 114.480401 \r\nL 119.139982 110.814866 \r\nL 119.152168 111.65564 \r\nL 119.267942 95.953119 \r\nL 119.207009 113.349287 \r\nL 119.286222 100.290063 \r\nL 119.465977 111.050767 \r\nL 119.469023 109.998286 \r\nL 119.511677 108.117129 \r\nL 119.548237 112.677877 \r\nL 119.660964 116.845457 \r\nL 119.612217 110.578965 \r\nL 119.673151 115.974439 \r\nL 119.761505 114.2445 \r\nL 119.694478 118.496762 \r\nL 119.779785 116.101462 \r\nL 119.804158 118.285056 \r\nL 119.880326 114.861471 \r\nL 119.990006 111.776615 \r\nL 119.926026 119.198415 \r\nL 120.00524 112.865388 \r\nL 120.03266 113.343238 \r\nL 120.063127 109.217999 \r\nL 120.081407 109.804727 \r\nL 120.224601 96.805991 \r\nL 120.300768 90.636279 \r\nL 120.346468 94.773615 \r\nL 120.349515 95.160734 \r\nL 120.407402 88.482929 \r\nL 120.413495 87.751031 \r\nL 120.462242 91.186714 \r\nL 120.486615 90.866131 \r\nL 120.489662 92.075878 \r\nL 120.510989 86.692503 \r\nL 120.599343 91.985147 \r\nL 120.718163 95.729316 \r\nL 120.64809 86.166262 \r\nL 120.727303 94.767566 \r\nL 120.840031 89.263216 \r\nL 120.858311 89.946723 \r\nL 120.995412 97.525791 \r\nL 120.904011 87.956689 \r\nL 121.001505 97.283841 \r\nL 121.022832 96.461213 \r\nL 121.031972 98.58432 \r\nL 121.175166 108.607077 \r\nL 121.178213 107.609035 \r\nL 121.303127 96.309994 \r\nL 121.312267 97.380621 \r\nL 121.315313 97.846374 \r\nL 121.379294 93.823964 \r\nL 121.40976 95.475269 \r\nL 121.525534 103.653161 \r\nL 121.458507 92.196853 \r\nL 121.586468 102.36478 \r\nL 121.592561 101.052204 \r\nL 121.662635 106.048461 \r\nL 121.693102 103.066434 \r\nL 121.711382 106.314606 \r\nL 121.790596 102.721656 \r\nL 121.796689 103.362822 \r\nL 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107.699766 \r\nL 123.639932 97.652814 \r\nL 123.655166 101.052204 \r\nL 123.7161 94.555861 \r\nL 123.74352 95.922875 \r\nL 123.75266 94.912736 \r\nL 123.82578 104.022134 \r\nL 123.83492 101.602639 \r\nL 123.920227 107.445719 \r\nL 123.947647 103.03619 \r\nL 123.950694 102.89102 \r\nL 123.972021 106.223874 \r\nL 123.975068 106.169436 \r\nL 124.051235 109.012342 \r\nL 123.999441 104.143109 \r\nL 124.084748 107.246111 \r\nL 124.124355 105.068566 \r\nL 124.103028 108.165519 \r\nL 124.194429 107.088844 \r\nL 124.200522 107.482012 \r\nL 124.206615 106.15129 \r\nL 124.212709 106.786407 \r\nL 124.310203 103.75599 \r\nL 124.255362 108.849026 \r\nL 124.328483 104.475789 \r\nL 124.343716 103.18136 \r\nL 124.39551 109.078878 \r\nL 124.401603 108.921611 \r\nL 124.407697 109.242194 \r\nL 124.41379 110.373308 \r\nL 124.48691 103.489845 \r\nL 124.489957 103.719697 \r\nL 124.648385 112.502464 \r\nL 124.50519 103.374919 \r\nL 124.654478 111.558861 \r\nL 124.718458 107.7784 \r\nL 124.748925 112.829095 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129.230596 86.450553 \r\nL 129.264109 92.577924 \r\nL 129.34637 97.979446 \r\nL 129.306763 91.229055 \r\nL 129.38293 95.426879 \r\nL 129.398163 89.015218 \r\nL 129.498704 93.043676 \r\nL 129.559638 87.914347 \r\nL 129.605338 92.432754 \r\nL 129.651038 89.67453 \r\nL 129.721112 95.838193 \r\nL 129.803372 85.779143 \r\nL 129.836886 92.813824 \r\nL 129.861259 93.442893 \r\nL 129.882586 86.093678 \r\nL 129.97094 76.657648 \r\nL 129.900866 88.755122 \r\nL 130.004453 77.673836 \r\nL 130.028827 76.331016 \r\nL 130.07148 87.206645 \r\nL 130.242095 120.027092 \r\nL 130.245141 118.883881 \r\nL 130.333495 129.826047 \r\nL 130.373102 121.980835 \r\nL 130.528483 98.935146 \r\nL 130.531529 99.298071 \r\nL 130.601603 94.682884 \r\nL 130.635117 88.277272 \r\nL 130.699097 99.419045 \r\nL 130.702144 99.11056 \r\nL 130.711284 102.945459 \r\nL 130.796591 94.440935 \r\nL 130.799638 95.106296 \r\nL 130.827058 91.356079 \r\nL 130.918458 91.725052 \r\nL 130.921505 92.825922 \r\nL 130.979392 87.593764 \r\nL 131.025092 91.537541 \r\nL 131.079932 89.015218 \r\nL 131.098213 93.563868 \r\nL 131.119539 93.430796 \r\nL 131.131726 94.99137 \r\nL 131.186566 88.343808 \r\nL 131.22008 91.622223 \r\nL 131.226173 90.835887 \r\nL 131.27492 95.106296 \r\nL 131.360227 98.560125 \r\nL 131.317574 93.104164 \r\nL 131.390694 96.406774 \r\nL 131.402881 94.31996 \r\nL 131.451628 100.205381 \r\nL 131.485141 99.122657 \r\nL 131.488188 99.709385 \r\nL 131.555215 93.787671 \r\nL 131.567402 94.07801 \r\nL 131.674036 80.359475 \r\nL 131.713643 81.000641 \r\nL 131.80809 90.212867 \r\nL 131.747156 79.506603 \r\nL 131.838557 85.767046 \r\nL 131.841603 85.724705 \r\nL 131.847697 86.65621 \r\nL 131.984797 98.475442 \r\nL 131.987844 96.727357 \r\nL 132.060964 107.391281 \r\nL 132.088385 102.388975 \r\nL 132.091431 102.31639 \r\nL 132.106665 104.318522 \r\nL 132.112758 102.739802 \r\nL 132.243765 122.204638 \r\nL 132.246812 120.341627 \r\nL 132.252905 118.02496 \r\nL 132.277279 123.831748 \r\nL 132.350399 123.620042 \r\nL 132.356493 123.795456 \r\nL 132.359539 123.081705 \r\nL 132.40524 128.344106 \r\nL 132.49664 117.365648 \r\nL 132.499687 117.795108 \r\nL 132.56976 112.175832 \r\nL 132.825682 93.406601 \r\nL 132.575854 113.028704 \r\nL 132.831775 95.172832 \r\nL 132.837869 95.553902 \r\nL 132.840915 94.434886 \r\nL 132.871382 91.610126 \r\nL 132.947549 95.753511 \r\nL 132.996296 88.827707 \r\nL 133.06637 93.557819 \r\nL 133.300964 105.425441 \r\nL 133.087697 92.100073 \r\nL 133.319244 105.032273 \r\nL 133.361898 94.828054 \r\nL 133.441112 100.277966 \r\nL 133.450252 100.973571 \r\nL 133.456345 98.493589 \r\nL 133.462439 95.795852 \r\nL 133.520326 106.375093 \r\nL 133.553839 103.090629 \r\nL 133.556886 102.897069 \r\nL 133.575166 107.990106 \r\nL 133.578213 107.736059 \r\nL 133.715313 120.964647 \r\nL 133.733593 119.053246 \r\nL 133.767107 129.033662 \r\nL 133.840227 118.248764 \r\nL 133.855461 119.295195 \r\nL 133.879834 113.016606 \r\nL 133.882881 113.167825 \r\nL 134.071775 97.955251 \r\nL 134.093102 101.723614 \r\nL 134.105289 103.27814 \r\nL 134.190596 100.011822 \r\nL 134.196689 97.646765 \r\nL 134.294183 101.772004 \r\nL 134.29723 101.40908 \r\nL 134.464797 76.923792 \r\nL 134.571431 72.012218 \r\nL 134.540964 77.135498 \r\nL 134.583618 73.234063 \r\nL 134.623225 75.756386 \r\nL 134.601898 71.963828 \r\nL 134.690252 73.923619 \r\nL 134.699392 72.012218 \r\nL 134.778606 82.464435 \r\nL 134.888286 90.624182 \r\nL 134.827353 81.974488 \r\nL 134.900473 88.712781 \r\nL 135.037574 80.123574 \r\nL 135.04062 80.395767 \r\nL 135.071087 84.696419 \r\nL 135.110694 79.028753 \r\nL 135.165534 84.000815 \r\nL 135.189908 82.319266 \r\nL 135.226468 87.80547 \r\nL 135.272168 85.482755 \r\nL 135.290448 85.017002 \r\nL 135.305682 89.214826 \r\nL 135.360522 87.865957 \r\nL 135.366615 89.505165 \r\nL 135.439736 85.452511 \r\nL 135.467156 84.490762 \r\nL 135.494576 88.997071 \r\nL 135.531136 88.960779 \r\nL 135.66519 109.992238 \r\nL 135.668237 108.87927 \r\nL 135.683471 107.548548 \r\nL 135.747451 117.293063 \r\nL 135.765731 113.506554 \r\nL 135.768778 113.597285 \r\nL 135.780964 111.208034 \r\nL 135.927205 89.487019 \r\nL 136.055166 70.481887 \r\nL 136.070399 73.572792 \r\nL 136.149613 84.871833 \r\nL 136.186173 76.107213 \r\nL 136.19836 73.959911 \r\nL 136.274527 85.355732 \r\nL 136.518262 107.076746 \r\nL 136.283667 83.033017 \r\nL 136.530448 104.070524 \r\nL 136.637082 98.269785 \r\nL 136.652316 98.638758 \r\nL 136.676689 103.489845 \r\nL 136.731529 94.89459 \r\nL 136.835117 84.279056 \r\nL 136.874724 88.035322 \r\nL 136.87777 89.202728 \r\nL 136.956984 82.077316 \r\nL 136.969171 82.416045 \r\nL 136.984404 81.351468 \r\nL 137.042291 89.359996 \r\nL 137.109318 93.775574 \r\nL 137.084945 85.912215 \r\nL 137.158065 90.787497 \r\nL 137.267746 95.112344 \r\nL 137.182439 89.553555 \r\nL 137.298213 91.92466 \r\nL 137.404846 79.464262 \r\nL 137.423127 83.940327 \r\nL 137.46578 87.678447 \r\nL 137.490154 82.10756 \r\nL 137.535854 85.083538 \r\nL 137.593741 83.97662 \r\nL 137.566321 87.684495 \r\nL 137.599834 86.438456 \r\nL 137.694281 94.840151 \r\nL 137.627254 85.688412 \r\nL 137.721701 90.231014 \r\nL 137.724748 88.984974 \r\nL 137.797869 95.100247 \r\nL 137.825289 91.622223 \r\nL 137.931923 95.735364 \r\nL 137.916689 91.513346 \r\nL 137.941063 94.138498 \r\nL 138.069023 79.252556 \r\nL 137.956296 95.330099 \r\nL 138.093397 81.726489 \r\nL 138.227451 93.182797 \r\nL 138.291431 96.71526 \r\nL 138.239638 91.622223 \r\nL 138.340178 93.769525 \r\nL 138.343225 93.588063 \r\nL 138.361505 98.445199 \r\nL 138.373692 99.794067 \r\nL 138.449859 92.590021 \r\nL 138.492512 87.055427 \r\nL 138.556493 92.807776 \r\nL 138.775854 109.865214 \r\nL 138.788041 107.850985 \r\nL 138.794134 107.965911 \r\nL 138.797181 107.554597 \r\nL 139.083569 87.255035 \r\nL 139.098802 88.132102 \r\nL 139.202389 93.340065 \r\nL 139.129269 84.817394 \r\nL 139.22067 93.049725 \r\nL 139.24809 91.283494 \r\nL 139.269416 94.731274 \r\nL 139.403471 107.0586 \r\nL 139.290743 93.21909 \r\nL 139.412611 105.274223 \r\nL 139.504011 98.578271 \r\nL 139.531431 101.342544 \r\nL 139.537525 103.689454 \r\nL 139.583225 95.318002 \r\nL 139.635018 99.431143 \r\nL 139.708139 97.132623 \r\nL 139.671579 102.328488 \r\nL 139.732512 100.683231 \r\nL 139.897033 117.268868 \r\nL 139.915313 114.323133 \r\nL 139.988434 119.36778 \r\nL 140.006714 116.966432 \r\nL 140.061554 118.956466 \r\nL 140.0189 115.944195 \r\nL 140.070694 118.732663 \r\nL 140.092021 124.757205 \r\nL 140.165141 117.438233 \r\nL 140.186468 121.702593 \r\nL 140.414969 105.165345 \r\nL 140.192561 122.355856 \r\nL 140.454576 107.82679 \r\nL 140.472856 111.964126 \r\nL 140.49723 105.364954 \r\nL 140.56121 105.588757 \r\nL 140.667844 112.079052 \r\nL 140.680031 109.756337 \r\nL 140.740964 115.284883 \r\nL 140.789711 110.754379 \r\nL 140.838458 110.808817 \r\nL 140.908532 106.411385 \r\nL 140.911579 106.598896 \r\nL 140.954232 102.927313 \r\nL 140.963372 103.151116 \r\nL 141.088286 92.571875 \r\nL 141.09438 94.235278 \r\nL 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284.011235 86.958647 \r\nL 284.075215 81.266785 \r\nL 284.148335 78.520659 \r\nL 284.093495 83.522964 \r\nL 284.190989 79.379579 \r\nL 284.200129 80.359475 \r\nL 284.264109 73.082844 \r\nL 284.367697 66.241722 \r\nL 284.379883 69.804429 \r\nL 284.501751 75.091025 \r\nL 284.41949 67.130887 \r\nL 284.507844 74.782539 \r\nL 284.584011 73.536499 \r\nL 284.550498 77.788762 \r\nL 284.623618 74.340981 \r\nL 284.626665 74.328884 \r\nL 284.629711 74.764393 \r\nL 284.663225 78.145637 \r\nL 284.730252 73.137283 \r\nL 284.733299 73.282452 \r\nL 284.846026 64.711392 \r\nL 284.748532 74.939806 \r\nL 284.900866 67.45147 \r\nL 284.910006 70.493985 \r\nL 284.97094 60.326058 \r\nL 284.99836 63.501645 \r\nL 285.02578 59.775623 \r\nL 285.101947 64.142811 \r\nL 285.108041 63.28389 \r\nL 285.150694 66.907084 \r\nL 285.181161 61.312002 \r\nL 285.220768 64.118616 \r\nL 285.330448 74.4922 \r\nL 285.357869 72.604994 \r\nL 285.379195 70.058476 \r\nL 285.427942 79.313043 \r\nL 285.430989 78.986411 \r\nL 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "def running_average(x,window):\n",
+    "    return np.convolve(x,np.ones(window)/window,mode='valid')\n",
+    "\n",
+    "plt.plot(running_average(rewards,100))"
+   ]
+  },
+  {
+   "source": [
+    "## Varying Hyperparameters and Seeing the Result in Action\n",
+    "\n",
+    "Now it would be interesting to actually see how the trained model behaves. Let's run the simulation, and we will be following the same action selection strategy as during training: sampling according to the probability distribution in Q-Table: "
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "obs = env.reset()\n",
+    "done = False\n",
+    "while not done:\n",
+    "   s = discretize(obs)\n",
+    "   env.render()\n",
+    "   v = probs(np.array(qvalues(s)))\n",
+    "   a = random.choices(actions,weights=v)[0]\n",
+    "   obs,_,done,_ = env.step(a)\n",
+    "env.close()"
+   ]
+  },
+  {
+   "source": [
+    "\n",
+    "## Saving result to an animated GIF\n",
+    "\n",
+    "If you want to impress your friends, you may want to send them the animated GIF picture of the balancing pole. To do this, we can invoke `env.render` to produce an image frame, and then save those to animated GIF using PIL library:"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "360\n"
+     ]
+    }
+   ],
+   "source": [
+    "from PIL import Image\n",
+    "obs = env.reset()\n",
+    "done = False\n",
+    "i=0\n",
+    "ims = []\n",
+    "while not done:\n",
+    "   s = discretize(obs)\n",
+    "   img=env.render(mode='rgb_array')\n",
+    "   ims.append(Image.fromarray(img))\n",
+    "   v = probs(np.array([Qbest.get((s,a),0) for a in actions]))\n",
+    "   a = random.choices(actions,weights=v)[0]\n",
+    "   obs,_,done,_ = env.step(a)\n",
+    "   i+=1\n",
+    "env.close()\n",
+    "ims[0].save('images/cartpole-balance.gif',save_all=True,append_images=ims[1::2],loop=0,duration=5)\n",
+    "print(i)"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/8-Reinforcement/README.md b/8-Reinforcement/README.md
index ac45459d..8e6996fd 100644
--- a/8-Reinforcement/README.md
+++ b/8-Reinforcement/README.md
@@ -32,8 +32,8 @@ The main difference between other types of machine learning and RL is that in RL
 
 ## Lessons
 
-1. [Introduction to Reinforcement Learning and Q-Learning](1-QLearning/README.md)
-2. [Using gym simulation environment](2-Gym/README.md)
+1. [Introduction to reinforcement learning and Q-Learning](1-QLearning/README.md)
+2. [Using a gym simulation environment](2-Gym/README.md)
 
 ## Credits