用分层强化学习优化协作智能体分组，提升团队效率。
Hierarchical Reinforcement Learning for Optimal Agent Grouping in Cooperative Systems
- 分层架构区分分组决策与个体动作
- 结合CTDE与置换不变网络实现高效协作
- 适合需要动态组队的多智能体系统
本文提出一种分层强化学习方法，用于解决协作多智能体系统中的智能体分组或配对问题。目标是同时学习最优分组策略与个体智能体行为策略。通过分层强化学习框架，将高层分组决策与低层智能体动作分离。采用CTDE（集中训练、分散执行）范式，保证学习效率与可扩展性。引入置换不变神经网络处理智能体同质性与协作关系，促进有效协调。对option-critic算法进行改进，以支持分层决策过程，实现动态且最优的策略调整。
原文摘要 · Abstract (English)
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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