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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 471–480 of 1718 papers

TitleStatusHype
Hierarchical Multi-agent Meta-Reinforcement Learning for Cross-channel Bidding—0
Tacit Learning with Adaptive Information Selection for Cooperative Multi-Agent Reinforcement Learning—0
AIR: Unifying Individual and Collective Exploration in Cooperative Multi-Agent Reinforcement Learning—0
Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement LearningCode0
Investigating Relational State Abstraction in Collaborative MARLCode0
The impact of behavioral diversity in multi-agent reinforcement learning—0
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
Achieving Collective Welfare in Multi-Agent Reinforcement Learning via Suggestion Sharing—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