ISSN:
0885-6125
Keywords:
Reinforcement learning
;
Markov Decision Problems
;
Temporal Difference Methods
;
Least-Squares
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
Notes:
Abstract We introduce two new temporal difference (TD) algorithms based on the theory of linear least-squares function approximation. We define an algorithm we call Least-Squares TD (LS TD) for which we prove probability-one convergence when it is used with a function approximator linear in the adjustable parameters. We then define a recursive version of this algorithm, Recursive Least-Square TD (RLS TD). Although these new TD algorithms require more computation per time-step than do Sutton‘s TD(λ) algorithms, they are more efficient in a statistical sense because they extract more information from training experiences. We describe a simulation experiment showing the substantial improvement in learning rate achieved by RLS TD in an example Markov prediction problem. To quantify this improvement, we introduce the TD error variance of a Markov chain, σTD, and experimentally conclude that the convergence rate of a TD algorithm depends linearly on σTD. In addition to converging more rapidly, LS TD and RLS TD do not have control parameters, such as a learning rate parameter, thus eliminating the possibility of achieving poor performance by an unlucky choice of parameters.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1023/A:1018056104778
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