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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 1551–1560 of 1718 papers

TitleStatusHype
“Other-Play” for Zero-Shot Coordination—0
OPtions as REsponses: Grounding behavioural hierarchies in multi-agent reinforcement learning—0
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning—0
Individual specialization in multi-task environments with multiagent reinforcement learners—0
Fairness in Multi-agent Reinforcement Learning for Stock Trading—0
Natural Actor-Critic Converges Globally for Hierarchical Linear Quadratic Regulator—0
Biases for Emergent Communication in Multi-agent Reinforcement Learning—0
Intelligent Coordination among Multiple Traffic Intersections Using Multi-Agent Reinforcement Learning—0
Decentralized Multi-Agent Reinforcement Learning with Networked Agents: Recent Advances—0
Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill DiscoveryCode0
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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