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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 1031–1040 of 1718 papers

TitleStatusHype
Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving—0
Safe Multi-Agent Reinforcement Learning via Shielding—0
Safe Multi-agent Reinforcement Learning with Natural Language Constraints—0
Safe Multi-Agent Reinforcement Learning with Convergence to Generalized Nash Equilibrium—0
Safety Constrained Multi-Agent Reinforcement Learning for Active Voltage Control—0
SA-MATD3:Self-attention-based multi-agent continuous control method in cooperative environments—0
Sample and Communication Efficient Fully Decentralized MARL Policy Evaluation via a New Approach: Local TD update—0
Sample-Efficient Multi-Agent Reinforcement Learning with Demonstrations for Flocking Control—0
Sample-Efficient Multi-Agent RL: An Optimization Perspective—0
Sample-efficient policy learning in multi-agent Reinforcement Learning via meta-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