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 941–950 of 1718 papers

TitleStatusHype
Cross-layer Band Selection and Routing Design for Diverse Band-aware DSA Networks—0
Crowd-sensing Enhanced Parking Patrol using Trajectories of Sharing Bikes—0
CuDA2: An approach for Incorporating Traitor Agents into Cooperative Multi-Agent Systems—0
Curiosity-driven Exploration in Sparse-reward Multi-agent Reinforcement Learning—0
Curriculum Learning for Cooperation in Multi-Agent Reinforcement Learning—0
CURO: Curriculum Learning for Relative Overgeneralization—0
DACOM: Learning Delay-Aware Communication for Multi-Agent Reinforcement Learning—0
Data-Driven Distributed Common Operational Picture from Heterogeneous Platforms using Multi-Agent Reinforcement Learning—0
DCIR: Dynamic Consistency Intrinsic Reward for Multi-Agent Reinforcement Learning—0
DCMAC: Demand-aware Customized Multi-Agent Communication via Upper Bound Training—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