Hierarchical Reinforcement Learning for Optimal Agent Grouping in Cooperative Systems
It addresses the agent grouping problem for multi-agent systems, which is incremental as it builds on existing hierarchical RL and CTDE methods.
This paper tackles the problem of simultaneously learning optimal agent grouping and policies in cooperative multi-agent systems, achieving improved coordination and scalability through a hierarchical reinforcement learning approach.
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.