SOTAVerified

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 541–550 of 1718 papers

TitleStatusHype
Data-Driven Distributed Common Operational Picture from Heterogeneous Platforms using Multi-Agent Reinforcement Learning—0
AutoRestTest: A Tool for Automated REST API Testing Using LLMs and MARL—0
DACOM: Learning Delay-Aware Communication for Multi-Agent Reinforcement Learning—0
Collaboration Between the City and Machine Learning Community is Crucial to Efficient Autonomous Vehicles Routing—0
A MARL-based Approach for Easing MAS Organization Engineering—0
CURO: Curriculum Learning for Relative Overgeneralization—0
Autonomous Vehicle Patrolling Through Deep Reinforcement Learning: Learning to Communicate and Cooperate—0
Curriculum Learning for Cooperation in Multi-Agent Reinforcement Learning—0
Autonomous Air Traffic Controller: A Deep Multi-Agent Reinforcement Learning Approach—0
A Local Information Aggregation based Multi-Agent Reinforcement Learning for Robot Swarm Dynamic Task Allocation—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