H∞ Optimal Control of Unknown Linear Discrete-time Systems: An Off-policy Reinforcement Learning Approach

This paper proposes a model-free Hos control design for linear discrete-time systems using reinforcement learning (RL). A novel off-policy RL algorithm is used to solve the game algebraic Riccati equation (GARE) online using the measured data along the system trajectories. The proposed RL algorithm has the following advantages compared to existing model-free RL methods for solving H-infinity control problem: 1) It is data efficient and fast since a stream of experiences which is obtained from executing a fixed behavioral policy is reused to update many value functions correspond to different leaning policies sequentially. 2) The disturbance input does not need to be adjusted in a specific manner. 3) There is no bias as a result of adding a probing noise to the control input to maintain persistence of excitation conditions. A simulation example is used to verify the effectiveness of the proposed control scheme.