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 391–400 of 1718 papers

TitleStatusHype
Distributionally Robust Multi-Agent Reinforcement Learning for Dynamic Chute Mapping—0
Enhancing Traffic Signal Control through Model-based Reinforcement Learning and Policy Reuse—0
Using a single actor to output personalized policy for different intersections—0
Q-MARL: A quantum-inspired algorithm using neural message passing for large-scale multi-agent reinforcement learning—0
Fully-Decentralized MADDPG with Networked Agents—0
Multi-Robot Collaboration through Reinforcement Learning and Abstract Simulation—0
Pretrained LLMs as Real-Time Controllers for Robot Operated Serial Production Line—0
Human Implicit Preference-Based Policy Fine-tuning for Multi-Agent Reinforcement Learning in USV Swarm—0
Decentralized Reinforcement Learning for Multi-Agent Multi-Resource Allocation via Dynamic Cluster Agreements—0
Towards Robust Multi-UAV Collaboration: MARL with Noise-Resilient Communication and Attention MechanismsCode0
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All datasetsParticleEnvs Cooperative CommunicationSMAC-ExpUAV Logistics

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified