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 141–150 of 1718 papers

Title	Date	Tasks	Status	Hype
Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement	Nov 2, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1
Teal: Learning-Accelerated Optimization of WAN Traffic Engineering	Oct 25, 2022	Graph Neural NetworkMulti-agent Reinforcement Learning	CodeCode Available	1
Solving Continuous Control via Q-learning	Oct 22, 2022	continuous-controlContinuous Control	CodeCode Available	1
Phantom -- A RL-driven multi-agent framework to model complex systems	Oct 12, 2022	Decision MakingMulti-agent Reinforcement Learning	CodeCode Available	1
Multiagent Reinforcement Learning Based on Fusion-Multiactor-Attention-Critic for Multiple-Unmanned-Aerial-Vehicle Navigation Control	Oct 10, 2022	Deep Reinforcement LearningMulti-agent Reinforcement Learning	CodeCode Available	1
ELIGN: Expectation Alignment as a Multi-Agent Intrinsic Reward	Oct 9, 2022	Multi-agent Reinforcement Learning	CodeCode Available	1
More Centralized Training, Still Decentralized Execution: Multi-Agent Conditional Policy Factorization	Sep 26, 2022	Multi-agent Reinforcement Learning	CodeCode Available	1
Towards a Standardised Performance Evaluation Protocol for Cooperative MARL	Sep 21, 2022	Decision MakingMulti-agent Reinforcement Learning	CodeCode Available	1
Formal Contracts Mitigate Social Dilemmas in Multi-Agent RL	Aug 22, 2022	ManagementMulti-agent Reinforcement Learning	CodeCode Available	1
AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N	Aug 15, 2022	EthicsMulti-agent Reinforcement Learning	CodeCode Available	1

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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