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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 1361–1370 of 1718 papers

TitleStatusHype
Offline Decentralized Multi-Agent Reinforcement Learning—0
Offline Learning in Markov Games with General Function Approximation—0
Offline Multi-Agent Reinforcement Learning with Coupled Value Factorization—0
Offline Multi-Agent Reinforcement Learning via In-Sample Sequential Policy Optimization—0
Offline Multi-agent Reinforcement Learning via Score Decomposition—0
Offline Pre-trained Multi-Agent Decision Transformer—0
Offline-to-Online Multi-Agent Reinforcement Learning with Offline Value Function Memory and Sequential Exploration—0
Off-Policy Action Anticipation in Multi-Agent Reinforcement Learning—0
Offsetting Unequal Competition through RL-assisted Incentive Schemes—0
On Diagnostics for Understanding Agent Training Behaviour in Cooperative MARL—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