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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 661–670 of 1718 papers

TitleStatusHype
Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces—0
MABL: Bi-Level Latent-Variable World Model for Sample-Efficient Multi-Agent Reinforcement Learning—0
An Abstraction-based Method to Check Multi-Agent Deep Reinforcement-Learning Behaviors—0
Deep Multi-Agent Reinforcement Learning for Decentralized Active Hypothesis Testing—0
Bilateral Deep Reinforcement Learning Approach for Better-than-human Car Following Model—0
Deep Multi-Agent Reinforcement Learning Based Cooperative Edge Caching in Wireless Networks—0
Biases for Emergent Communication in Multi-agent Reinforcement Learning—0
A Multi-Agent Reinforcement Learning Testbed for Cognitive Radio Applications—0
A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning—0
Achieving Optimal Tissue Repair Through MARL with Reward Shaping and Curriculum 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