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Vishvanathan K 3 years ago committed by GitHub
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@ -229,8 +229,7 @@ We are now ready to implement the learning algorithm. Before we do that, we also
We add a few `eps` to the original vector in order to avoid division by 0 in the initial case, when all components of the vector are identical.
Run them learning algorithm through 5000 experiments, also called **epochs**: (code block 8)
```python
```python
for epoch in range(5000):
# Pick initial point
@ -255,11 +254,11 @@ Run them learning algorithm through 5000 experiments, also called **epochs**: (c
ai = action_idx[a]
Q[x,y,ai] = (1 - alpha) * Q[x,y,ai] + alpha * (r + gamma * Q[x+dpos[0], y+dpos[1]].max())
n+=1
```
```
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 an arrow head.
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 an arrow head.
<img src="images/learned.png"/>
<img src="images/learned.png"/>
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