|Reinforcement Q-learning Based on Multirate Generalized Policy Iteration and Its Application to a 2-DOF Helicopter
Tae Yoon Chun, Jin Bae Park*, and Yoon Ho Choi
International Journal of Control, Automation, and Systems, vol. 16, no. 1, pp.377-386, 2018
Abstract : "In this paper, we propose a novel Q-learning method based on multirate generalized policy iteration
(MGPI) for unknown discrete-time (DT) linear quadratic regulation (LQR) problems. Q-learning is an effective
scheme for unknown dynamical systems because it does not require any knowledge of the system dynamics to
solve optimal control problems. By applying the MGPI concept, which is an extension of basic GPI with multirate
time horizon steps, a new Q-learning algorithm is proposed for solving the LQR problem. Further, it is proven that
the proposed algorithm converges to an optimal solution i.e., it learns the optimal control policy iteratively using the
states and the control-input information. Finally, we employ the two degree-of-freedom helicopter model to verify
the effectiveness of the proposed method and investigate its convergence properties."
Adaptive optimal control, linear quadratic regulation, multirate generalized policy iteration, Q-learning.
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