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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 741–750 of 1718 papers

TitleStatusHype
Causality Detection for Efficient Multi-Agent Reinforcement Learning—0
Learning Reward Machines in Cooperative Multi-Agent Tasks—0
Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games—0
Large-Scale Traffic Signal Control Using Constrained Network Partition and Adaptive Deep Reinforcement Learning—0
Cheap Talk Discovery and Utilization in Multi-Agent Reinforcement Learning—0
Boundary-aware Supervoxel-level Iteratively Refined Interactive 3D Image Segmentation with Multi-agent Reinforcement Learning—0
Major-Minor Mean Field Multi-Agent Reinforcement Learning—0
A New Policy Iteration Algorithm For Reinforcement Learning in Zero-Sum Markov Games—0
Decentralized Multi-Agent Reinforcement Learning for Continuous-Space Stochastic GamesCode0
SVDE: Scalable Value-Decomposition Exploration for Cooperative Multi-Agent Reinforcement Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified