提出一种自适应事件触发强化学习控制方法，可同步学习控制与通信策略。
Adaptive Event-triggered Reinforcement Learning Control for Complex Nonlinear Systems
- 通过扩展状态空间融合累积奖励，实现触发条件的自适应学习。
- 在含界不确定性的非线性系统中显著减少通信次数与计算开销。
- 适合需要低通信频率的复杂动态系统控制，如工业机器人、无人系统。
本文针对具有界不确定性且交互复杂的连续时间非线性系统，提出一种自适应事件触发强化学习控制方法。该方法能够同步学习控制策略与通信策略，从而降低参数数量和计算开销。通过将累积奖励作为状态空间的扩展分量，无需显式学习触发条件即可实现准确高效的触发决策，进而获得自适应的非平稳策略。最后，通过多个数值实验验证了所提方法的有效性。
原文摘要 · Abstract (English)
In this paper, we propose an adaptive event-triggered reinforcement learning control for continuous-time nonlinear systems, subject to bounded uncertainties, characterized by complex interactions. Specifically, the proposed method is capable of jointly learning both the control policy and the communication policy, thereby reducing the number of parameters and computational overhead when learning them separately or only one of them. By augmenting the state space with accrued rewards that represent the performance over the entire trajectory, we show that accurate and efficient determination of triggering conditions is possible without the need for explicit learning triggering conditions, thereby leading to an adaptive non-stationary policy. Finally, we provide several numerical examples to demonstrate the effectiveness of the proposed approach.
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