Reinforcement learning implementations

This repository contains the implementation of two RL algorithms: Proximal Policy Optimization and Deep Q Learning both from scratch (with PyTorch) and using the TorchRL library.

The environment is the car racing simulation from OpenAI's Gym package.

Set up

Create a virtual environment:

conda create -n NAME_OF_THE_ENVIRONEMNT python=3.8
conda activate NAME_OF_THE_ENVIRONEMNT

Clone the repository:

git clone https://github.com/bielnebot/rl_implementations.git

And install the requirements:

pip install -r requirements.txt

Use

To train a policy

To test a pre-trained policy

To-Do

• PPO with TorchRL
• DQN with TorchRL
• DQN from scratch

Done

• PPO from scratch