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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 531–540 of 1718 papers

TitleStatusHype
Minimax Exploiter: A Data Efficient Approach for Competitive Self-Play—0
Multi-Agent Reinforcement Learning for Power Control in Wireless Networks via Adaptive Graphs—0
Directly Attention Loss Adjusted Prioritized Experience Replay—0
Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approach—0
Tactics2D: A Highly Modular and Extensible Simulator for Driving Decision-makingCode2
JaxMARL: Multi-Agent RL Environments and Algorithms in JAXCode2
Joint User Pairing and Beamforming Design of Multi-STAR-RISs-Aided NOMA in the Indoor Environment via Multi-Agent Reinforcement Learning—0
Multi-agent Attacks for Black-box Social Recommendations—0
Multi-Agent Quantum Reinforcement Learning using Evolutionary OptimizationCode0
Enhancing Multi-Agent Coordination through Common Operating Picture Integration—0
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All datasetsParticleEnvs Cooperative CommunicationSMAC-ExpUAV Logistics

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified