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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

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)

qlib

Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant investment strategies. An increasing number of SOTA Quant research works/papers are released in Qlib.