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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 1121–1130 of 1718 papers

TitleStatusHype
Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning—0
Taming Equilibrium Bias in Risk-Sensitive Multi-Agent Reinforcement Learning—0
TAR^2: Temporal-Agent Reward Redistribution for Optimal Policy Preservation in Multi-Agent Reinforcement Learning—0
TarMAC: Targeted Multi-Agent Communication—0
Task Assignment and Exploration Optimization for Low Altitude UAV Rescue via Generative AI Enhanced Multi-agent Reinforcement Learning—0
Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning—0
Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning—0
M3HF: Multi-agent Reinforcement Learning from Multi-phase Human Feedback of Mixed Quality—0
The Benefits of Power Regularization in Cooperative Reinforcement Learning—0
The challenge of hidden gifts in multi-agent reinforcement learning—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