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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 1641–1650 of 1718 papers

TitleStatusHype
Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning—0
Distributed Policy Iteration for Scalable Approximation of Cooperative Multi-Agent Policies—0
The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) CompetitionCode0
Theory of Minds: Understanding Behavior in Groups Through Inverse Planning—0
Multi-agent Reinforcement Learning Embedded Game for the Optimization of Building Energy Control and Power System Planning—0
Optimizing Market Making using Multi-Agent Reinforcement Learning—0
Malthusian Reinforcement Learning—0
Likelihood Quantile Networks for Coordinating Multi-Agent Reinforcement Learning—0
Communication-Efficient Policy Gradient Methods for Distributed Reinforcement Learning—0
Finite-Sample Analysis For Decentralized Batch Multi-Agent Reinforcement Learning With Networked Agents—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