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Example repository for the general organization of my code.

Official pytorch implementation of the paper <Model-based Multi-agent Policy Optimization with Adaptive Opponent-wise Rollouts>.

Multi-Agent Reinforcement Learning with JAX

PyTorch Tutorial for Deep Learning Researchers

Really Fast End-to-End Jax RL Implementations

JAX (Flax) implementation of algorithms for Deep Reinforcement Learning with continuous action spaces.

Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/

A library of reinforcement learning components and agents

Massively parallel rigidbody physics simulation on accelerator hardware.

High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)

Jax implementation of Proximal Policy Optimization (PPO) specifically tuned for Procgen, with benchmarked results and saved model weights on all environments.