This repository aims to solve three basic challenges in OpenAI Gym with RL techniques.

There are four different main files in this repository which are listed as follows.

1. LunarLander.ipynb
2. Mountain Car.ipynb
3. Pendulum.ipynb
4. Pendulum_Replay_Experince.ipynb

As their name suggest, each file is related to one specific RL problem. In fact, to solve these tasks different RL techniques were employed. The Lunar Lander problem was solved using Semi-gradient Expected Sarsa with experience replay, Mountain Car used Semi-gradient Sarsa with tile coding for linear features' construction. Furthermore, The Pendulum task was tackled with two approaches: The first one used Policy-gradient methods (Actor-Critic) without experience replay, sadly, this approach did not solved the challenge. The second approach used the same policy-gradient technique but with experience replay to increase stability during training and solve the challenge.

The file tiles3.py is a Tile Coding python implementation by Rich Sutton to create linear features for RL problems with function approximation.

The following list provides the minimum requirements to run the project.

matplotlib >= 3.2.1
cv2 >= 3.4.0
numpy >= 1.18.1
tqdm >= 4.46.1
torch >= 1.5.0
gym >= 0.17.2

To run the files, just use Jupyter Notebook and run the cells of the file you want to see.

jupyter-notebook

For further description and explanation, please check the Notebooks.

The algorithms on this repository are based on the following notes