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 1211–1220 of 1718 papers

TitleStatusHype
Model-based Multi-agent Reinforcement Learning: Recent Progress and Prospects—0
Model-based Reinforcement Learning for Service Mesh Fault Resiliency in a Web Application-level—0
Whole-Chain Recommendations—0
Improving Sample Efficiency of Model-Free Algorithms for Zero-Sum Markov Games—0
Modeling Fake News in Social Networks with Deep Multi-Agent Reinforcement Learning—0
Modeling Interactions of Autonomous Vehicles and Pedestrians with Deep Multi-Agent Reinforcement Learning for Collision Avoidance—0
Modeling Others using Oneself in Multi-Agent Reinforcement Learning—0
Modeling Sensorimotor Coordination as Multi-Agent Reinforcement Learning with Differentiable Communication—0
Modeling the Interaction between Agents in Cooperative Multi-Agent Reinforcement Learning—0
Trust Region Bounds for Decentralized PPO Under Non-stationarity—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