Introducing Deep RL Zoo
A collection of Deep Reinforcement Learning algorithms implemented with PyTorch to solve Atari games and classic control tasks like CartPole, LunarLander, and MountainCar
Reinforcement learning is a very exciting field in machine learning, with the success of the DQN agent playing Atari games at a human level [1], and AlphaGo playing the game of Go at a superhuman level [2].
We are excited to share our latest work Deep-RL-Zoo, an open-source implementation of common deep reinforcement learning algorithms, implemented with PyTorch. Our work is based on DQN Zoo [3], where we adapted the code from JAX to PyTorch. In addition, we have also implemented new state-of-the-art (SOTA) algorithms like PPO, RND, and Agent57.
This project is intended for educational and research purposes only, following the following principle:
Research-Friendly: we focus a detailed exploration of individual algorithms rather than creating a standard library.
Simple Code: We keep the code simple and mostly in a single file module, so you can easily adapt it to support other algorithms.
Low Dependencies: We only use basic tools like Numpy, PyTorch, and Gym for the project, with no reliance on third-party reinforcement learning libraries.