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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 951–960 of 1718 papers

TitleStatusHype
AC2C: Adaptively Controlled Two-Hop Communication for Multi-Agent Reinforcement Learning—0
Multi-Agent Reinforcement Learning with Common Policy for Antenna Tilt Optimization—0
Concept Learning for Interpretable Multi-Agent Reinforcement Learning—0
Semantic Information Marketing in The Metaverse: A Learning-Based Contract Theory Framework—0
MAC-PO: Multi-Agent Experience Replay via Collective Priority OptimizationCode0
Curiosity-driven Exploration in Sparse-reward Multi-agent Reinforcement Learning—0
Differentiable Arbitrating in Zero-sum Markov Games—0
Efficient Communication via Self-supervised Information Aggregation for Online and Offline Multi-agent Reinforcement Learning—0
AIIR-MIX: Multi-Agent Reinforcement Learning Meets Attention Individual Intrinsic Reward Mixing Network—0
Promoting Cooperation in Multi-Agent Reinforcement Learning via Mutual Help—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