Pytorch implementation of Deep Q Network (DQN) learning with vectorized environments
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Updated
Aug 30, 2024 - Python
Pytorch implementation of Deep Q Network (DQN) learning with vectorized environments
XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library
An elegant PyTorch deep reinforcement learning library.
Pytorch implementation of Proximal Policy Optimization (PPO) for discrete action spaces
[NeurIPS 2023 Spotlight] LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios (awesome MCTS)
This project is an implementation of the research paper called "Playing Atari with Deep Reinforcement Learning" by Mnih et al. 2013.
Transformers are Sample-Efficient World Models. ICLR 2023, notable top 5%.
DIAMOND (DIffusion As a Model Of eNvironment Dreams) is a reinforcement learning agent trained in a diffusion world model.
Python library for Reinforcement Learning.
High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
Pytorch implementation of Double Deep Q Network (DDQN) learning with vectorized environments
OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
PyTorch implementations for deep and classical rl algorithms
PyTorch implementation of Monte Carlo policy gradient reinforcement
A River Raid game written in Python
Unified Reinforcement Learning Framework
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