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+# Train Mountain Car
+
+[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.
+
+## A Mountain Car Environment
+
+[Mountain Car environment](https://gym.openai.com/envs/MountainCar-v0/) contains a car stuck in a valley:
+
+<img src="images/mountaincar.png" width="300"/>
+
+The goal is to get out of the valley and capture the flag, by doing at each step one of the following actions:
+
+| Value | Meaning |
+|---|---|
+| 0 | Accelerate to the left |
+| 1 | Do not accelerate |
+| 2 | Accelerate to the right |
+
+The main trick of this problem is, however, that the car's engine is not strong enough to scale the mountain in a single pass. Therefore, the only way to succeed is to drive back and forth to build up momentum.
+
+Observation space consists of just two values:
+
+| Num | Observation  | Min | Max |
+|-----|--------------|-----|-----|
+|  0  | Car Position | -1.2| 0.6 |
+|  1  | Car Velocity | -0.07 | 0.07 |
+
+Reward system for the mountain car is rather tricky:
+
+ * Reward of 0 is awarded if the agent reached the flag (position = 0.5) on top of the mountain.
+ * Reward of -1 is awarded if the position of the agent is less than 0.5.
+
+Episode terminates if the car position is more than 0.5, or episode length is greater than 200.
+## Instructions
+
+Adapt our reinforcement learning algorithm to solve the mountain car problem. Start with existing [notebook.ipynb](notebook.ipynb) code, substitute new environment, change state discretization functions, and try to make existing algorithm to train with minimal code modifications. Optimize the result by adjusting hyperparameters.
+
+> **Note**: Hyperparameters adjustment is likely to be needed to make algorithm converge. 
+## Rubric
+
+| Criteria | Exemplary | Adequate | Needs Improvement |
+| -------- | --------- | -------- | ----------------- |
+|          | Q-Learning algorithm is successfully adapted from CartPole example, with minimal code modifications, which is able to solve the problem of capturing the flag under 200 steps. | A new Q-Learning algorithm has been adopted from the Internet, but is well-documented; or existing algorithm adopted, but does not reach desired results | Student was not able to successfully adopt any algorithm, but has mede substantial steps towards solution (implemented state discretization, Q-Table data structure, etc.) |