Multi-agent Reinforcement Learning

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

Papers

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Showing 51–60 of 1718 papers

Title	Date	Tasks	Status	Hype	Score
Distributed Multi-Agent Reinforcement Learning with One-hop Neighbors and Compute Straggler Mitigation	Feb 18, 2022	Distributed ComputingMulti-agent Reinforcement Learning	CodeCode Available	1	5
Decentralized Social Navigation with Non-Cooperative Robots via Bi-Level Optimization	Jun 15, 2023	Collision AvoidanceMulti-agent Reinforcement Learning	CodeCode Available	1	5
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement Learning	May 28, 2019	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1	5
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning	Sep 22, 2023	counterfactualMulti-agent Reinforcement Learning	CodeCode Available	1	5
Battlesnake Challenge: A Multi-agent Reinforcement Learning Playground with Human-in-the-loop	Jul 20, 2020	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1	5
Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam Search	May 19, 2022	Decision MakingImage Captioning	CodeCode Available	1	5
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning	Nov 4, 2018	DecoderMulti-agent Reinforcement Learning	CodeCode Available	1	5
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning	Jun 7, 2021	Multi-agent Reinforcement LearningOffline RL	CodeCode Available	1	5
A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal Control	Mar 30, 2025	FairnessMulti-agent Reinforcement Learning	CodeCode Available	1	5
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence	Feb 7, 2023	Continuous ControlMuJoCo	CodeCode Available	1	5

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All datasets ParticleEnvs Cooperative Communication SMAC-Exp UAV Logistics

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	MATD3	final agent reward	-14	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DRIMA	Median Win Rate	15	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Fusion-Multi-Actor-Attention-Critic	Average Reward	39	—	Unverified