yuchen-x

MacroMARL

MacDeepMARL

MacDec-via-Cen

AdaPlanner

AdaPlanner: Language Models for Decision Making via Adaptive Planning from Feedback

DOP

Codes accompanying the paper "DOP: Off-Policy Multi-Agent Decomposed Policy Gradients" (ICLR 2021, https://arxiv.org/abs/2007.12322)

DynamiCrafter

[ECCV 2024] DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors

epymarl

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

flightmare

An Open Flexible Quadrotor Simulator

gifs

gym-macro-overcooked

gym-pybullet-drones

PyBullet Gym environments for single and multi-agent reinforcement learning of quadcopter control

lb-foraging

A multi-agent environment for RL

LICA-1

[NeurIPS 2020] PyTorch implementation of "Learning Implicit Credit Assignment for Cooperative Muti-Agent Reinforcement Learning"

liir

Learning Individual Intrinsic Reward in MARL

Lissy93

🥳 Hello World!

MAAC

Code for "Actor-Attention-Critic for Multi-Agent Reinforcement Learning" ICML 2019

maddpg

Code for the MADDPG algorithm from the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"

open-review-toolkit

Open Review Toolkit: Better books, Higher sales, Increased access to knowledge

overcooked_ai

A benchmark environment for fully cooperative human-AI performance.

pointw.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

pymarl

Python Multi-Agent Reinforcement Learning framework

ReAct

[ICLR 2023] ReAct: Synergizing Reasoning and Acting in Language Models

reflexion

[NeurIPS 2023] Reflexion: Language Agents with Verbal Reinforcement Learning

rl_games

RL implementations

robotic-warehouse

Multi-Robot Warehouse (RWARE): A multi-agent reinforcement learning environment

ROLA

ROMA

Codes accompanying the paper "ROMA: Multi-Agent Reinforcement Learning with Emergent Roles" (ICML 2020 https://arxiv.org/abs/2003.08039)

SQDDPG

This is a framework for the research on multi-agent reinforcement learning and the implementation of the experiments in the paper titled by ''Shapley Q-value: A Local Reward Approach to Solve Global Reward Games''.

turtlebot_setup

VDACs

Value-Decomposition Multi-Agent Actor-Critics