bmazoure/ppo_jax
Jax implementation of Proximal Policy Optimization (PPO) specifically tuned for Procgen, with benchmarked results and saved model weights on all environments.
This project offers a JAX-based implementation of the Proximal Policy Optimization (PPO) algorithm, specifically tailored for reinforcement learning tasks within the OpenAI Procgen environments. It takes in game environment definitions and outputs trained PPO policies, along with pre-trained weights for various Procgen games. It's designed for machine learning researchers and practitioners focused on developing and evaluating agents for procedurally generated game environments.
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Use this if you are a researcher or practitioner working with reinforcement learning and need a stable, benchmarked JAX implementation of PPO for OpenAI's Procgen game environments.
Not ideal if you are looking for a general-purpose PPO implementation for non-Procgen environments or if you are not familiar with JAX, Flax, and Optax.
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Last pushed
Aug 04, 2022
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