Implementation of Efficient Off-policy Meta-learning via Probabilistic Context Variables (PEARL)

An elegant, flexible, and superfast PyTorch deep reinforcement learning platform.

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the PaLM architecture. Basically ChatGPT but with PaLM

Implementation of benchmark RL algorithms

PyTorch implementation of SAC-Discrete.

Deep Reinforcement Learning Lab, a platform designed to make DRL technology and fun for everyone

A curated list of Diffusion Model in RL resources (continually updated)

Code for the paper "Planning with Diffusion for Flexible Behavior Synthesis"

PyTorch implementation of Deep Reinforcement Learning: Policy Gradient methods (TRPO, PPO, A2C) and Generative Adversarial Imitation Learning (GAIL). Fast Fisher vector product TRPO.

Intro to Reinforcement Learning (强化学习纲要）

Video Diffusion Alignment via Reward Gradients. We improve a variety of video diffusion models such as VideoCrafter, OpenSora, ModelScope and StableVideoDiffusion by finetuning them using various r…

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