Create README.md

README.md

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+# Monte Carlo Q Learning on Simple Self Defined Environment
+
+## Environment
+
+it is like below:
+**(a simple grid based env)**
+
+```
+ E  .  .  .
+ .  .  . .
+ S  .  .  .
+ .  .  .  E
+```
+Where, 
+* E --> terminal / end point
+* S --> start point --> initially at (2, 0)
+
+### How it works
+
+The agent starts from **S** and learns the optimal policy to reach the end point. the reward are set as below:
+```
+(0, 0) --> 0
+(3, 3) --> 0
+```
+The overall grid with initial defined rewards look like below:
+```
+---------------------------
+ 0.00|-1.00|-1.00|-1.00|
+---------------------------
+-1.00|-1.00|-1.00|-1.00|
+---------------------------
+-1.00|-1.00|-1.00|-1.00|
+---------------------------
+-1.00|-1.00|-1.00| 0.00|
+```
+Possible actions that the agent can take are:
+* U (up)
+* D (down)
+* L (left)
+* R (right)
+
+### Final Q table after 10000 Episodes
+```
+Q table: 
+----------------------------------------------
+| State  | U      | D      | L      | R      |
+----------------------------------------------
+| (0, 0) |   0.00 |   0.00 |   0.00 |   0.00 |
+| (0, 1) |  -1.18 |  -2.67 |   0.00 |  -2.56 |
+| (0, 2) |  -2.83 |  -4.45 |  -1.52 |  -4.76 |
+| (0, 3) |  -4.03 |  -7.43 |  -3.41 |  -6.30 |
+| (1, 0) |   0.00 |  -2.71 |  -1.46 |  -2.65 |
+| (1, 1) |  -1.46 |  -3.76 |  -1.45 |  -3.74 |
+| (1, 2) |  -2.70 |  -4.87 |  -3.69 |  -4.93 |
+| (1, 3) |  -4.31 |  -6.26 |  -9.94 |  -8.55 |
+| (2, 0) |  -1.45 |  -3.63 |  -2.64 |  -3.62 |
+| (2, 1) |  -2.77 |  -4.08 |  -2.69 |  -4.50 |
+| (2, 2) |  -3.81 |  -4.37 |  -4.89 |  -5.44 |
+| (2, 3) |  -7.41 |   0.00 |  -5.34 |  -6.04 |
+| (3, 0) |  -2.65 |  -3.76 |  -3.69 |  -4.39 |
+| (3, 1) |  -4.35 |  -4.93 |  -3.50 |  -3.99 |
+| (3, 2) | -10.00 |  -9.99 |  -5.64 |   0.00 |
+| (3, 3) |   0.00 |   0.00 |   0.00 |   0.00 |
+----------------------------------------------
+```
+The agent chooses the maximum value for each state to find the optimal policy. 
+## Reference
+* I learned it from the repository described [here](https://github.com/ravasconcelos/monte_carlo) and [here](https://colab.research.google.com/drive/1yJwMgv3XSZ6mLcZ1W7JAxLRZVBjHOXea?usp=sharing)