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awesome-deep-rl

A curated list of awesome Deep Reinforcement Learning resources.

awesome-rl-envs

beachlanding

catalyst

Accelerated deep learning R&D

cleanrl

High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)

coupon-center

crying-babay

cs-notes

:books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计

epymarl

An extension of the PyMARL codebase that includes additional algorithms and environment support

finrl

FinRL: Financial Reinforcement Learning Framework. Please star. 🔥

gametheory

博弈论基础的课程主页

hexo-theme-cactus

:cactus: A responsive, clean and simple theme for Hexo.

imitation

Clean PyTorch implementations of imitation and reward learning algorithms

llms-from-scratch

Implementing a ChatGPT-like LLM from scratch, step by step

luorq3.github.io

Personal Blog

macce

malib

A parallel framework for population-based multi-agent reinforcement learning.

marllib

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

mate

MATE: the Multi-Agent Tracking Environment.

maze_q_learning_solver

minis

ml-agents

The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.

mo-gym

Multi-objective gym environments for reinforcement learning.

morl-baselines

Multi-Objective Reinforcement Learning algorithms implementations.

multi-agent-combat-env

multi-agent-combat-env

off-policy

PyTorch implementations of popular off-policy multi-agent reinforcement learning algorithms, including QMix, VDN, MADDPG, and MATD3.

on-policy

This is the official implementation of Multi-Agent PPO (MAPPO).

pettingzoo

A standard API for multi-agent reinforcement learning environments, with popular reference environments and related utilities

pymarl

Python Multi-Agent Reinforcement Learning framework

pymarl2

Fine-tuned MARL algorithms on SMAC (100% win rates on most scenarios)