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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 1231–1240 of 1718 papers

TitleStatusHype
Transfer Learning in Multi-Agent Reinforcement Learning with Double Q-Networks for Distributed Resource Sharing in V2X Communication—0
Q-SMASH: Q-Learning-based Self-Adaptation of Human-Centered Internet of Things—0
Restless and Uncertain: Robust Policies for Restless Bandits via Deep Multi-Agent Reinforcement Learning—0
Mava: a research library for distributed multi-agent reinforcement learning in JAXCode1
Traffic Signal Control with Communicative Deep Reinforcement Learning Agents: a Case Study—0
Collaborative Visual NavigationCode1
SA-MATD3:Self-attention-based multi-agent continuous control method in cooperative environments—0
Federated Dynamic Spectrum Access—0
MMD-MIX: Value Function Factorisation with Maximum Mean Discrepancy for Cooperative Multi-Agent Reinforcement Learning—0
Scientific multi-agent reinforcement learning for wall-models of turbulent flows—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