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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 211–220 of 1718 papers

TitleStatusHype
The impact of behavioral diversity in multi-agent reinforcement learning—0
Investigating Relational State Abstraction in Collaborative MARLCode0
Agent-Temporal Credit Assignment for Optimal Policy Preservation in Sparse Multi-Agent Reinforcement Learning—0
Heterogeneous Multi-Agent Reinforcement Learning for Distributed Channel Access in WLANs—0
Coordinated Power Smoothing Control for Wind Storage Integrated System with Physics-informed Deep Reinforcement Learning—0
A MARL Based Multi-Target Tracking Algorithm Under Jamming Against RadarCode1
Achieving Collective Welfare in Multi-Agent Reinforcement Learning via Suggestion Sharing—0
Cluster-Based Multi-Agent Task Scheduling for Space-Air-Ground Integrated Networks—0
GTDE: Grouped Training with Decentralized Execution for Multi-agent Actor-Critic—0
Quantum-Train-Based Distributed 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