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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 201–210 of 1718 papers

TitleStatusHype
Generative Emergent Communication: Large Language Model is a Collective World Model—0
Distributed Traffic Control in Complex Dynamic Roadblocks: A Multi-Agent Deep RL Approach—0
Game Theory and Multi-Agent Reinforcement Learning : From Nash Equilibria to Evolutionary Dynamics—0
Scalable Hierarchical Reinforcement Learning for Hyper Scale Multi-Robot Task Planning—0
Hierarchical Multi-agent Meta-Reinforcement Learning for Cross-channel Bidding—0
SMAC-Hard: Enabling Mixed Opponent Strategy Script and Self-play on SMACCode1
Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement LearningCode0
Multi Agent Reinforcement Learning for Sequential Satellite Assignment ProblemsCode1
Tacit Learning with Adaptive Information Selection for Cooperative Multi-Agent Reinforcement Learning—0
AIR: Unifying Individual and Collective Exploration in Cooperative 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