Multi Player H Differential Game Using On Policy And Off Policy Reinforcement Learning
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Multi-player H∞ Differential Game Using On-policy and Off-policy Reinforcement Learning
Author | : Peiliang An |
Publisher | : |
Total Pages | : 27 |
Release | : 2022 |
Genre | : |
ISBN | : |
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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.
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