Discovering and Achieving Goals via World Models, NeurIPS 2021

PyTorch implementation of Soft Actor-Critic + Autoencoder(SAC+AE)

Collections of robotics environments geared towards benchmarking multi-task and meta reinforcement learning

StarCraft II Client - protocol definitions used to communicate with StarCraft II.

[NeurIPS'21 Outstanding Paper] Library for reliable evaluation on RL and ML benchmarks, even with only a handful of seeds.

Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL

An educational resource to help anyone learn deep reinforcement learning.

StarCraft II Learning Environment

SMAC: The StarCraft Multi-Agent Challenge

Fine-tuned MARL algorithms on SMAC (100% win rates on most scenarios)

用基于策略梯度得强化学习方法训练AI玩王者荣耀

Rainbow is all you need! A step-by-step tutorial from DQN to Rainbow

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

Multi-Joint dynamics with Contact. A general purpose physics simulator.

Policy Gradient is all you need! A step-by-step tutorial for well-known PG methods.

Open-source codebase for EfficientZero, from "Mastering Atari Games with Limited Data" at NeurIPS 2021.

CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning

Author's PyTorch implementation of TD3+BC, a simple variant of TD3 for offline RL

MineRL Competition for Sample Efficient Reinforcement Learning - Python Package

DrQ-v2: Improved Data-Augmented Reinforcement Learning

基于深度强化学习的原神自动钓鱼AI

DrQ: Data regularized Q

Mastering Atari with Discrete World Models

PyTorch implementation of DreamerV2 model-based RL algorithm

Critic Guided Segmentation of Rewarding Objects in First-Person Views. Explanatory video:

ForgER algorithm

OpenAI Gym wrapper for the DeepMind Control Suite

Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.

DMControl Generalization Benchmark