Popular GitHub Repositories for Atari 2600

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.

[NeurIPS 2023 Spotlight] LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios (awesome MCTS)

C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.

Mastering Atari with Discrete World Models

Python library for Reinforcement Learning.

SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference. Implements IMPALA and R2D2 algorithms in TF2 with SEED's architecture.

Unified Reinforcement Learning Framework

A curated list of Monte Carlo tree search papers with implementations.

A3C LSTM Atari with Pytorch plus A3G design

Reaver: Modular Deep Reinforcement Learning Framework. Focused on StarCraft II. Supports Gym, Atari, and MuJoCo.

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

A binary release of trained deep reinforcement learning models trained in the Atari machine learning benchmark, and a software release that enables easy visualization and analysis of models, and comparison across training algorithms.

Pytorch LSTM RNN for reinforcement learning to play Atari games from OpenAI Universe. We also use Google Deep Mind's Asynchronous Advantage Actor-Critic (A3C) Algorithm. This is much superior and efficient than DQN and obsoletes it. Can play on many games

A high-performance Atari A3C agent in 180 lines of PyTorch

Transformers are Sample-Efficient World Models. ICLR 2023, notable top 5%.