Adaptive Event-triggered Reinforcement Learning Control for Complex Nonlinear Systems

TL;DR

This paper introduces an adaptive event-triggered reinforcement learning control method for complex nonlinear systems, enabling joint learning of control and communication policies to improve efficiency and reduce computational costs.

Contribution

It presents a novel approach that combines adaptive event-triggered control with reinforcement learning, jointly learning control and communication policies for nonlinear systems.

Findings

Effective reduction in communication and computation overhead.

Accurate triggering conditions without explicit learning triggers.

Numerical examples demonstrating improved control performance.

Abstract

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.