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noreward-rl 是用 Python 编写的学习资料，用于系统学习与查阅。

[ICML 2017] TensorFlow code for Curiosity-driven Exploration for Deep Reinforcement Learning

softlearning 是用 Python 编写的学习资料，用于系统学习与查阅。

Softlearning is a reinforcement learning framework for training maximum entropy policies in continuous domains. Includes the official implementation…

DI-star 是用 Python 编写的学习资料，用于系统学习与查阅，主打可伸缩。

An artificial intelligence platform for the StarCraft II with large-scale distributed training and grand-master agents.

DRL-robot-navigation 是用 Python 编写的学习资料，用于系统学习与查阅。

Deep Reinforcement Learning for mobile robot navigation in ROS Gazebo simulator. Using Twin Delayed Deep Deterministic Policy Gradient (TD3) neural …

SLM-Lab 是用 Python 编写的学习资料，用于系统学习与查阅。

Modular Deep Reinforcement Learning framework in PyTorch. Companion library of the book "Foundations of Deep Reinforcement Learning".

MARLlib 是用 Python 编写的学习资料，用于系统学习与查阅。

One repository is all that is necessary for Multi-agent Reinforcement Learning (MARL)

pytorch-a3c 是用 Python 编写的学习资料，用于系统学习与查阅，主打异步并发。

PyTorch implementation of Asynchronous Advantage Actor Critic (A3C) from "Asynchronous Methods for Deep Reinforcement Learning".

deep-q-learning 是用 Python 编写的学习资料，用于系统学习与查阅，主打轻量。

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

PyTorch-RL 是用 Python 编写的学习资料，用于系统学习与查阅，主打速度快。

PyTorch implementation of Deep Reinforcement Learning: Policy Gradient methods (TRPO, PPO, A2C) and Generative Adversarial Imitation Learning (GAIL)…

omnisafe 是用 Python 编写的学习资料，用于系统学习与查阅。

JMLR: OmniSafe is an infrastructural framework for accelerating SafeRL research.

purejaxrl 是用 Python 编写的学习资料，用于系统学习与查阅，主打速度快。

Really Fast End-to-End Jax RL Implementations

DexterousHands 是用 Python 编写的学习资料，用于系统学习与查阅。

This is a library that provides dual dexterous hand manipulation tasks through Isaac Gym

visual-pushing-grasping 是用 Python 编写的 AI 工具，用于人工智能方向的建模与推理。

Train robotic agents to learn to plan pushing and grasping actions for manipulation with deep reinforcement learning.

sumo-rl 是用 Python 编写的学习资料，用于系统学习与查阅。

Reinforcement Learning environments for Traffic Signal Control with SUMO. Compatible with Gymnasium, PettingZoo, and popular RL libraries.

DeepRL-Tutorials 是用 Jupyter Notebook 编写的学习资料，用于系统学习与查阅，主打实现清晰。

Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch

PyGame-Learning-Environment 是用 Python 编写的学习资料，用于系统学习与查阅。

PyGame Learning Environment (PLE) -- Reinforcement Learning Environment in Python.

gdrl 是用 Jupyter Notebook 编写的学习资料，用于系统学习与查阅。

Grokking Deep Reinforcement Learning

Deep-Reinforcement-Learning-Algorithms 是用 Jupyter Notebook 编写的学习资料，用于系统学习与查阅。

32 projects in the framework of Deep Reinforcement Learning algorithms: Q-learning, DQN, PPO, DDPG, TD3, SAC, A2C and others. Each project is provid…

btgym 是用 Python 编写的交易类项目，用于商城、支付与交易场景，主打可伸缩。

Scalable, event-driven, deep-learning-friendly backtesting library

rl-book 是用 HTML 编写的学习资料，用于系统学习与查阅。

Source codes for the book "Reinforcement Learning: Theory and Python Implementation"