Improved Temporal Difference Methods with Linear Function Approximation
We consider temporal difference algorithms within the context of infinite-horizon finite-state dynamic programming problems with discounted cost, and linear cost function approximation. We show, under standard assumptions, that a least squares-based temporal difference method, proposed by Nedic and Bertsekas (NeB03), converges with a stepsize equal to 1. To our knowl- edge, this is the first iterative temporal difference method that converges without requiring a diminishing stepsize. We discuss the connections of the method with Sutton's TD(λ) and with various versions of least squares-based value iteration, and we show via analysis and experiment that the method is substantially and often dramatically faster than TD(λ), as well as simpler and more reliable. We also discuss the relation of our method with the LSTD method of Boyan (Boy02), and Bradtke and Barto (BrB96).