Data-Based Predictive Control via Multistep Policy Gradient Reinforcement Learning

In this article, a model-free predictive control algorithm for the real-time system is presented. The algorithm is data driven and is able to improve system performance based on multistep policy gradient reinforcement learning. By learning from the offline dataset and real-time data, the knowledge of system dynamics is avoided in algorithm design and application. Cooperative games of the multiplayer in time horizon are presented to model the predictive control as optimization problems of multiagent and guarantee the optimality of the predictive control policy. In order to implement the algorithm, neural networks are used to approximate the action-state value function and predictive control policy, respectively. The weights are determined by using the methods of weighted residual. Numerical results show the effectiveness of the proposed algorithm.

Automated summary

This article presents a model-free predictive control algorithm for real-time systems that improves system performance through data-driven multi-step policy gradient reinforcement learning. By learning from offline and real-time data, the algorithm avoids the need for knowledge of system dynamics in its design and application. Cooperative games are used to model predictive control as multi-agent optimization problems and ensure the optimal predictive control policy. Neural networks are employed to approximate the action-state value function and predictive control policy, with weights determined using weighted residual methods. Numerical results demonstrate the effectiveness of the proposed algorithm.