rl-coursework

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)

Minimal Deep Q Learning (DQN & DDQN) implementations in Keras

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.