MULTI-PLAYER H1 DIFFERENTIAL GAME USING ON-POLICY AND OFF-POLICY REINFORCEMENT LEARNING

Author

Peiliang An

Graduation Semester and Year

2021

Language

English

Document Type

Thesis

Degree Name

Master of Science in Electrical Engineering

Department

Electrical Engineering

First Advisor

Yan Wan

Abstract

This work studies a multi-player H-infinity 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 o -policy IRL, are developed to solve the differential game online. We show that the off-policy IRL can solve the multi-player H-infinity 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.

Keywords

Differential game, Worst-case disturbance, Reinforcement learning

Disciplines

Electrical and Computer Engineering | Engineering

License

This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.

Comments

Degree granted by The University of Texas at Arlington

Recommended Citation

An, Peiliang, "MULTI-PLAYER H1 DIFFERENTIAL GAME USING ON-POLICY AND OFF-POLICY REINFORCEMENT LEARNING" (2021). Electrical Engineering Theses. 339.
https://mavmatrix.uta.edu/electricaleng_theses/339