Hierarchical Reinforcement Learning for Optimal Agent Grouping in Cooperative Systems

• URL: http://arxiv.org/abs/2501.06554v1
• Date: Sat, 11 Jan 2025 14:22:10 GMT
• Title: Hierarchical Reinforcement Learning for Optimal Agent Grouping in Cooperative Systems
• Authors: Liyuan Hu,
• Abstract summary: This paper presents a hierarchical reinforcement learning (RL) approach to address the agent grouping or pairing problem in cooperative multi-agent systems. By employing a hierarchical RL framework, we distinguish between high-level decisions of grouping and low-level agents' actions. We incorporate permutation-in neural networks to handle the homogeneity and cooperation among agents, enabling effective coordination.
• Score: 0.4759142872591625
• License:
• Abstract: This paper presents a hierarchical reinforcement learning (RL) approach to address the agent grouping or pairing problem in cooperative multi-agent systems. The goal is to simultaneously learn the optimal grouping and agent policy. By employing a hierarchical RL framework, we distinguish between high-level decisions of grouping and low-level agents' actions. Our approach utilizes the CTDE (Centralized Training with Decentralized Execution) paradigm, ensuring efficient learning and scalable execution. We incorporate permutation-invariant neural networks to handle the homogeneity and cooperation among agents, enabling effective coordination. The option-critic algorithm is adapted to manage the hierarchical decision-making process, allowing for dynamic and optimal policy adjustments.

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