Adaptive Event-triggered Reinforcement Learning Control for Complex Nonlinear Systems
This work addresses the problem of efficient control and communication for complex nonlinear systems, which is relevant for engineers and control system designers.
This paper introduces an adaptive event-triggered reinforcement learning control for continuous-time nonlinear systems with bounded uncertainties. The method jointly learns control and communication policies, which reduces parameters and computational overhead compared to separate learning or learning only one policy.
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.