Title :
Incremental least squares policy iteration in reinforcement learning for control
Author :
Li, Chun-Gui ; Wang, Meng ; Yang, Shu-Hong
Author_Institution :
Dept. of Comput. Eng., Guangxi Univ. of Technol., Liuzhou
Abstract :
We propose a novel algorithm of reinforcement learning for control problems which combines value-function approximation with linear architectures and approximate policy iteration. This algorithm improves least-squares policy iteration (LSPI) methods by using incremental least-squares temporal-difference learning algorithm (iLSTD) for prediction problems. We show that the novel algorithm has less computing complexities than LSPI, and has the same performance as LSPI in learning optimal policies.
Keywords :
adaptive control; iterative methods; learning (artificial intelligence); learning systems; least squares approximations; approximate policy iteration; incremental least squares policy iteration; incremental least-squares temporal-difference learning algorithm; linear architectures; prediction problems; reinforcement learning; value-function approximation; Computer architecture; Convergence; Cybernetics; Electronic mail; Function approximation; Least squares approximation; Least squares methods; Linear approximation; Machine learning; Scheduling algorithm; Linear function approximation; incremental updating; least-squares methods; policy evaluation; policy iteration;
Conference_Titel :
Machine Learning and Cybernetics, 2008 International Conference on
Conference_Location :
Kunming
Print_ISBN :
978-1-4244-2095-7
Electronic_ISBN :
978-1-4244-2096-4
DOI :
10.1109/ICMLC.2008.4620736
