Multi Agent Reinforcement Learning


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

Enhancing Multi-Agent Systems via Reinforcement Learning with LLM-based Planner and Graph-based Policy

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Mar 13, 2025
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Distributionally Robust Multi-Agent Reinforcement Learning for Dynamic Chute Mapping

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Mar 12, 2025
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ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement Learning

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Mar 12, 2025
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Enhancing Traffic Signal Control through Model-based Reinforcement Learning and Policy Reuse

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Mar 11, 2025
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Large-scale Regional Traffic Signal Control Based on Single-Agent Reinforcement Learning

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Mar 12, 2025
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RESTRAIN: Reinforcement Learning-Based Secure Framework for Trigger-Action IoT Environment

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Mar 12, 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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Multi-Agent LLM Actor-Critic Framework for Social Robot Navigation

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Mar 12, 2025
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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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Using a single actor to output personalized policy for different intersections

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Mar 10, 2025
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