Merge pull request #19 from joseph-jnl/gridworld

update ch3 docs with images

docs/chap3_finite_mdps.md

@@ -3,21 +3,23 @@
 ```bash
 python run.py
 ```
+![image](https://github.com/user-attachments/assets/216229c6-5925-4479-9cd7-52a62f4ad4fb)
 /// caption
 Figure 3.2: State-value function for a random policy (equal probability for all directions). Config uses this example as a default.
 ///
 
 ```bash
 python run.py grid=example_3_8_optimal_grid plots=example_3_8_optimal_grid
 ```
-
+![image](https://github.com/user-attachments/assets/9b2a7a65-5f23-4b64-9217-f96b20dd8ebd)
 /// caption
 Figure 3.5: Optimal state-value function and policy for a gridworld.
 ///
 
 ```bash
 python run.py grid.n_rows=10 grid.n_cols=10 grid.special_states=[[0,0,8,1],[1,3,7,9]] grid.special_states_prime=[[4,2,1,8],[1,3,7,1]] grid.special_states_rewards=[10,5,8,15] plots.policy=true
 ```
+![image](https://github.com/user-attachments/assets/0a808f1d-e82b-4f31-9d4a-cc8b9833313d)
 /// caption
 Example of creating a new gridworld with arbitrary size and rewards.
 ///

docs/index.md

@@ -12,4 +12,4 @@ python run.py -m run.steps=1000 run.n_runs=2000 +bandit.epsilon=0,0.01,0.1 +band
 Figure 2.3 (rlbook): The `+bandit.random_argmax=true` flag was used to switch over to an argmax implementation that randomizes between tiebreakers rather than first occurence used in the default numpy implementation to better align with the original example.
 [Link to wandb artifact](https://api.wandb.ai/links/josephjnl/53gxgbcc)
 
-Further details on experimental setup and results can be found corresponding chapter docs.
+Further details on experimental setup and results can be found within the corresponding chapter docs.