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
Multi-Agent Reinforcement Learning with Graph Convolutional Neural Networks for optimal Bidding Strategies of Generation Units in Electricity Markets—0
Heterogeneous Multi-agent Zero-Shot Coordination by CoevolutionCode0
Multi-agent reinforcement learning for intent-based service assurance in cellular networks—0
Learning to Coordinate for a Worker-Station Multi-robot System in Planar Coverage Tasks—0
Transferable Multi-Agent Reinforcement Learning with Dynamic Participating Agents—0
Efficiently Computing Nash Equilibria in Adversarial Team Markov Games—0
Deep Reinforcement Learning for Multi-Agent InteractionCode2
Heterogeneous-Agent Mirror Learning: A Continuum of Solutions to Cooperative MARL—0
Multi-Agent Reinforcement Learning for Long-Term Network Resource Allocation through Auction: a V2X Application—0
INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks—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