Off-Policy Integral Reinforcement Learning Method to Solve Nonlinear Continuous-Time Multiplayer Nonzero-Sum Games

作者：Ruizhuo Song, Frank L. Lewis, Qinglai Wei · 发表于：IEEE Transactions on Neural Networks and Learning Systems · 年份：2016 · DOI：10.1109/tnnls.2016.2582849 · 被引用次数：233 · 研究领域：Adaptive Dynamic Programming Control、Viral Infections and Vectors、Reinforcement Learning in Robotics

This paper establishes an off-policy integral reinforcement learning (IRL) method to solve nonlinear continuous-time (CT) nonzero-sum (NZS) games with unknown system dynamics. The IRL algorithm is presented to obtain the iterative control and off-policy learning is used to allow the dynamics to be completely unknown. Off-policy IRL is designed to do policy evaluation and policy improvement in the policy iteration algorithm. Critic and action networks are used to obtain the performance index and control for each player. The gradient descent algorithm makes the update of critic and action weights simultaneously. The convergence analysis of the weights is given. The asymptotic stability of the closed-loop system and the existence of Nash equilibrium are proved. The simulation study demonstrates the effectiveness of the developed method for nonlinear CT NZS games with unknown system dynamics.