Multi Agent Reinforcement Learning


Multi-agent reinforcement learning is the process of training multiple agents to interact and collaborate in a shared environment.

A Cascading Cooperative Multi-agent Framework for On-ramp Merging Control Integrating Large Language Models

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Mar 11, 2025
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Q-MARL: A quantum-inspired algorithm using neural message passing for large-scale multi-agent reinforcement learning

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Mar 10, 2025
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Using a single actor to output personalized policy for different intersections

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Mar 10, 2025
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Fully-Decentralized MADDPG with Networked Agents

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Mar 09, 2025
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Adaptive routing protocols for determining optimal paths in AI multi-agent systems: a priority- and learning-enhanced approach

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Mar 10, 2025
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HIPPO-MAT: Decentralized Task Allocation Using GraphSAGE and Multi-Agent Deep Reinforcement Learning

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Mar 08, 2025
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Human Implicit Preference-Based Policy Fine-tuning for Multi-Agent Reinforcement Learning in USV Swarm

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Mar 07, 2025
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M3HF: Multi-agent Reinforcement Learning from Multi-phase Human Feedback of Mixed Quality

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Mar 06, 2025
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Vairiational Stochastic Games

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Mar 08, 2025
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Multi-Robot Collaboration through Reinforcement Learning and Abstract Simulation

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Mar 07, 2025
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