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JAX - A curated list of resources https://github.com/google/jax
High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
jax version of ppo algorithm in mujoco enviroment, achieve SOTA(tianshou)
🕹️ A diverse suite of scalable reinforcement learning environments in JAX
This is the homepage of a new book entitled "Mathematical Foundations of Reinforcement Learning."
An implementation of DreamerV2 written in JAX, with support for running multiple random seeds of an experiment on a single GPU.
A curated list of Multi-Modal Reinforcement Learning resources (continually updated)
🏛️A research-friendly codebase for fast experimentation of single-agent reinforcement learning in JAX • End-to-End JAX RL
This repository contains a collection of resources and papers on Diffusion Models for RL, accompanying the paper "Diffusion Models for Reinforcement Learning: A Survey"
JMLR: OmniSafe is an infrastructural framework for accelerating SafeRL research.
The repository is for safe reinforcement learning baselines.
A curated list of awesome model based RL resources (continually updated)
🤖 Elegant implementations of offline safe RL algorithms in PyTorch
国家自然科学基金申请书正文（面上项目）LaTeX 模板（非官方）
The implementation for the paper Continuous Deep Q-Learning in Optimal Control Problems: Normalized Advantage Functions Analysis // NeurIPS 2022
High-quality single-file implementations of SOTA Offline and Offline-to-Online RL algorithms: AWAC, BC, CQL, DT, EDAC, IQL, SAC-N, TD3+BC, LB-SAC, SPOT, Cal-QL, ReBRAC
Graphic notes on Gilbert Strang's "Linear Algebra for Everyone"
Official codebase for "B-Pref: Benchmarking Preference-BasedReinforcement Learning" contains scripts to reproduce experiments.
Repository to accompany RSS 2018 paper on dexterous hand manipulation
A large-scale benchmark and learning environment.
PyTorch implementations of deep reinforcement learning algorithms and environments