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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 791–800 of 1718 papers

TitleStatusHype
Human Machine Co-adaption Interface via Cooperation Markov Decision Process System—0
Human Implicit Preference-Based Policy Fine-tuning for Multi-Agent Reinforcement Learning in USV Swarm—0
Human and Multi-Agent collaboration in a human-MARL teaming framework—0
Curriculum Learning for Cooperation in Multi-Agent Reinforcement Learning—0
Autonomous Air Traffic Controller: A Deep Multi-Agent Reinforcement Learning Approach—0
A MARL-based Approach for Easing MAS Organization Engineering—0
A Local Information Aggregation based Multi-Agent Reinforcement Learning for Robot Swarm Dynamic Task Allocation—0
Bidirectional Distillation: A Mixed-Play Framework for Multi-Agent Generalizable Behaviors—0
Late Breaking Results: Breaking Symmetry- Unconventional Placement of Analog Circuits using Multi-Level Multi-Agent Reinforcement Learning—0
How much can change in a year? Revisiting Evaluation in 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