无需系统模型，用强化学习自动分配通信优先级
Priority-Driven Control and Communication in Decentralized Multi-Agent Systems via Reinforcement Learning
- 基于强化学习联合学习通信优先级与控制策略
- 在基准任务上性能优于传统基线方法
- 适合缺乏精确模型的分布式多智能体系统
事件触发控制可避免网络化多智能体系统中通信带宽的过度使用。然而，大多数现有方法依赖于精确的系统模型，这在实际中可能不可用。本文提出一种无模型、优先级驱动的强化学习算法，可在去中心化多智能体系统中从数据中联合学习通信优先级和控制策略。通过学习通信优先级，我们规避了事件触发控制中典型的二元通信决策带来的混合动作空间问题。我们在基准任务上评估了该算法，并证明其性能优于基线方法。
原文摘要 · Abstract (English)
Event-triggered control provides a mechanism for avoiding excessive use of constrained communication bandwidth in networked multi-agent systems. However, most existing methods rely on accurate system models, which may be unavailable in practice. In this work, we propose a model-free, priority-driven reinforcement learning algorithm that learns communication priorities and control policies jointly from data in decentralized multi-agent systems. By learning communication priorities, we circumvent the hybrid action space typical in event-triggered control with binary communication decisions. We evaluate our algorithm on benchmark tasks and demonstrate that it outperforms the baseline method.
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