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Multi-agent Reinforcement Learning

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The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.

Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/xylee95/md-pgt

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MDPGT: Momentum-based Decentralized Policy Gradient Tracking

https://github.com/yamoling/lle

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Laser Learning Environment: A new environment for coordination-critical multi-agent tasks

https://github.com/yanQval/SOP-CG

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Self-Organized Polynomial-Time Coordination Graphs

https://github.com/yang-xy20/async_mappo

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Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration

https://github.com/yang-xy20/mage-x

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Learning Graph-Enhanced Commander-Executor for Multi-Agent Navigation

https://github.com/yangmin32/GPMF

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Partially Observable Mean Field Multi-Agent Reinforcement Learning Based on Graph-Attention

https://github.com/yannbouteiller/eola

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Evolution of Societies via Reinforcement Learning

https://github.com/ydchen0806/dbmim

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Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning

https://github.com/yeshenpy/pmic

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PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration

https://github.com/ying-wen/gr2

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Modelling Bounded Rationality in Multi-Agent Interactions by Generalized Recursive Reasoning

https://github.com/yiqinyang/icq

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Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning

https://github.com/yoavalon/MultiQuadcopterReinforcmentLearning

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Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Coordination by Multi-Critic Policy Gradient Optimization

https://github.com/yokian/distspectrl

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DistSPECTRL: Distributing Specifications in Multi-Agent Reinforcement Learning Systems

https://github.com/youpengzhao/crashed_agent

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Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed Agents

https://github.com/ysmei97/mac-po

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MAC-PO: Multi-Agent Experience Replay via Collective Priority Optimization

https://github.com/yuanjiayiy/InvestESG

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InvestESG: A multi-agent reinforcement learning benchmark for studying climate investment as a social dilemma

https://github.com/yuelin301/informationdesignmarl

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Information Design in Multi-Agent Reinforcement Learning

https://github.com/yurui-li/ctc

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The Composite Task Challenge for Cooperative Multi-Agent Reinforcement Learning

https://github.com/yuweisunn/homogeneous-learning

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Homogeneous Learning: Self-Attention Decentralized Deep Learning

https://github.com/zdhnarsil/stochastic-marginal-actor-critic

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Latent State Marginalization as a Low-cost Approach for Improving Exploration

https://github.com/zhang-shaowei/smmae

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Self-Motivated Multi-Agent Exploration

https://github.com/zhaoyl18/ratio_game

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Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning

https://github.com/zhengyinan-air/omiga

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Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization

https://github.com/zhiyuanyaoj/marllb

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Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential Game

https://github.com/zhuliwen/pku_traffic_lights

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Learning to Share in Multi-Agent Reinforcement Learning

https://github.com/zhuliwen/roadnetsz

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MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control

https://github.com/zilin-zhao/iros25-supp

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Towards Robust Multi-UAV Collaboration: MARL with Noise-Resilient Communication and Attention Mechanisms

https://github.com/ziyuwan/rema-public

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ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement Learning

https://github.com/zowiezhang/stas

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STAS: Spatial-Temporal Return Decomposition for Multi-agent Reinforcement Learning

https://github.com/zyh1999/cadp

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Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?

https://github.com/zyr17/unilight

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Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Method

https://github.com/zzq-bot/romance

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Robust multi-agent coordination via evolutionary generation of auxiliary adversarial attackers

https://github.itap.purdue.edu/Clan-labs/MeanFieldwithGlobalState

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Mean-Field Control based Approximation of Multi-Agent Reinforcement Learning in Presence of a Non-decomposable Shared Global State

https://github.itap.purdue.edu/Clan-labs/NonUniformMFC

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Can Mean Field Control (MFC) Approximate Cooperative Multi Agent Reinforcement Learning (MARL) with Non-Uniform Interaction?

https://gitlab.com/AAAL/DFRL

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Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning

https://gitlab.com/ericavandersar/marl4powergridtopo

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Multi-Agent Reinforcement Learning for Power Grid Topology Optimization

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