SOTAVerified

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 741–750 of 1718 papers

TitleStatusHype
Cooperation and Competition: Flocking with Evolutionary Multi-Agent Reinforcement Learning—0
Decentralized Deep Reinforcement Learning for Network Level Traffic Signal Control—0
Influence-Based Reinforcement Learning for Intrinsically-Motivated Agents—0
Information-Bottleneck-Based Behavior Representation Learning for Multi-agent Reinforcement learning—0
Few is More: Task-Efficient Skill-Discovery for Multi-Task Offline Multi-Agent Reinforcement Learning—0
Decentralized Graph-Based Multi-Agent Reinforcement Learning Using Reward Machines—0
Information Structure in Mappings: An Approach to Learning, Representation, and Generalisation—0
Metric Policy Representations for Opponent Modeling—0
Integrating independent and centralized multi-agent reinforcement learning for traffic signal network optimization—0
Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for 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