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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 871–880 of 1718 papers

TitleStatusHype
Fleet Rebalancing for Expanding Shared e-Mobility Systems: A Multi-agent Deep Reinforcement Learning ApproachCode1
Efficient Domain Coverage for Vehicles with Second-Order Dynamics via Multi-Agent Reinforcement Learning—0
Decentralized Policy Optimization—0
Scalable Multi-Agent Reinforcement Learning through Intelligent Information AggregationCode1
Multi-Agent Reinforcement Learning for Adaptive Mesh RefinementCode1
Agent-Time Attention for Sparse Rewards Multi-Agent Reinforcement LearningCode0
LearningGroup: A Real-Time Sparse Training on FPGA via Learnable Weight Grouping for Multi-Agent Reinforcement Learning—0
MAPDP: Cooperative Multi-Agent Reinforcement Learning to Solve Pickup and Delivery Problems—0
Non-Linear Coordination Graphs—0
Entity Divider with Language Grounding in Multi-Agent Reinforcement Learning—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