An elegant PyTorch deep reinforcement learning library.
High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).
OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
DIAMOND (DIffusion As a Model Of eNvironment Dreams) is a reinforcement learning agent trained in a diffusion world model. NeurIPS 2024 Spotlight.
[NeurIPS 2023 Spotlight] LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios (awesome MCTS)
XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library
Mastering Atari with Discrete World Models
Python library for Reinforcement Learning.
Transformers are Sample-Efficient World Models. ICLR 2023, notable top 5%.
Unified Reinforcement Learning Framework
SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference. Implements IMPALA and R2D2 algorithms in TF2 with SEED's architecture.
A curated list of Monte Carlo tree search papers with implementations.
A3C LSTM Atari with Pytorch plus A3G design
AI research environment for the Atari 2600 games 🤖.
Reinforcement learning with tensorflow 2 keras
Reinforcement learning framework to accelerate research
Open source implementation of the PAAC algorithm presented in Efficient Parallel Methods for Deep Reinforcement Learning
Deep recurrent Q Learning using Tensorflow, openai/gym and openai/retro
A high-performance Atari A3C agent in 180 lines of PyTorch
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