Multi-player H∞ Differential Game Using On-policy and Off-policy Reinforcement Learning

Download or read book Multi-player H∞ Differential Game Using On-policy and Off-policy Reinforcement Learning written by Peiliang An and published by . This book was released on 2022 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work studies a multi-player H∞ differential game for systems of general linear dynamics. In this game, multiple players design their control inputs to minimize their cost functions in the presence of worst-case disturbances. We first derive the optimal control and disturbance policies using the solutions to Hamilton-Jacobi-Isaacs (HJI) equations. We then prove that the derived optimal policies stabilize the system and constitute a Nash equilibrium solution. Two integral reinforcement learning (IRL) -based algorithms, including the policy iteration IRL and off-policy IRL, are developed to solve the differential game online. We show that the off-policy IRL can solve the multi-player H∞ differential game online without using any system dynamics information. Simulation studies are conducted to validate the theoretical analysis and demonstrate the effectiveness of the developed learning algorithms.