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 691–700 of 1718 papers

TitleStatusHype
Achieving Collective Welfare in Multi-Agent Reinforcement Learning via Suggestion Sharing—0
Explaining Strategic Decisions in Multi-Agent Reinforcement Learning for Aerial Combat Tactics—0
Environment Complexity and Nash Equilibria in a Sequential Social Dilemma—0
Decentralized Policy Optimization—0
Decentralized multi-agent reinforcement learning algorithm using a cluster-synchronized laser network—0
Decentralized Multi-agent Reinforcement Learning based State-of-Charge Balancing Strategy for Distributed Energy Storage System—0
Decentralized Multi-Agent Reinforcement Learning with Global State Prediction—0
A Multi-Agent Reinforcement Learning Approach For Safe and Efficient Behavior Planning Of Connected Autonomous Vehicles—0
Decentralized Multi-Agent Reinforcement Learning: An Off-Policy Method—0
Decentralized Multi-Agent Reinforcement Learning for Task Offloading Under Uncertainty—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