Reinforcement-learning-exercises-with-OpenAI-Gym
A recap on RL used for this exercises with the cheatsheet.
More theory about Reinforcement Learning can be find here.
Monte_carlo.pdf : Summary of the Monte Carlo method.
Temporal difference methods summary.pdf : Summary of the temporal difference methods.
Using the Monte carlo methods, we will solve the Black Jack environment.
Monte_Carlo.ipynb : the Jupyter notebook where Monte Carlo methods are used to solve the environment.
plot_util.py : contains a plotting function for visualizing state-value functions and policies.
Optimal policy we are looking for:
The result we have:
Implementation of Sarsa, Q-Learning and Expected Sarsa in order to solve the CliffWalking environment.
Temporal_Difference_Methods.ipynb : Implementation of the three methods.
plot_utils.py : contains a plotting function for visualizing state-value functions and policies.
check_test.py : contains unit tests to check the validity of your implementations.
The goal is to obtain state-value functions for Sarsa, Q-learning and Expected Sarsa:
Average reward:
Work based on the part 3.1 of this paper to solve the Taxi-v2 environment.
agent.py: The reinforcement learning agent is developed here.
monitor.py: The interact function tests how well your agent learns from interaction with the environment.
main.py: Run this file in the terminal to check the performance of your agent.
When you run main.py, the agent that specify in agent.py interacts with the environment for 20,000 episodes. The details of the interaction are specified in monitor.py, which returns two variables: avg_rewards and best_avg_reward. The best_avg_reward is used to see how well the agent performed in the task.
Discretizing a continuous environement, (MountainCar-v0) , in order to apply reinforcement learning algorithms.
Discretization.ipynb : Implementation of the technique.
Discretization process:
Score evolution while training:
Q-Table result:
Tile coding is an innovative way of discretizing a continuous space that enables better generalization compared to a single grid-based approach. The fundamental idea is to create several overlapping grids or tilings; then for any given sample value, you need only check which tiles it lies in. You can then encode the original continuous value by a vector of integer indices or bits that identifies each activated tile.
Tile coding on the Acrobot-v1 environement in order to apply rienforcement learning.
Tile_Coding_Solution.ipynb : Implementation of the technique.
Tile coding process:
Score evolution while training:
