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 271–280 of 1718 papers

Title	Date	Tasks	Status	Hype	Score
Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networks	Jun 3, 2023	Federated LearningMulti-agent Reinforcement Learning	CodeCode Available	1	5
Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement	Nov 2, 2022	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1	5
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
Communicative Reinforcement Learning Agents for Landmark Detection in Brain Images	Aug 18, 2020	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1	5
Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning	Apr 14, 2021	counterfactualDeep Reinforcement Learning	CodeCode Available	1	5
Multi-Agent Trust Region Learning	Jan 1, 2021	Atari GamesMuJoCo	CodeCode Available	1	5
Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks	Oct 27, 2021	Multi-agent Reinforcement Learningreinforcement-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
Actor-Attention-Critic for Multi-Agent Reinforcement Learning	Oct 5, 2018	Multi-agent Reinforcement Learningreinforcement-learning	CodeCode Available	1	5
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning	Jun 21, 2020	FPS GamesGeneral Reinforcement Learning	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