Basis Function Adaptation Methods for Cost Approximation in MDP
Huizhen Yu and Dimitri Bertsekas
In: IEEE International Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL) 2009, Nashville, USA(2009).

Abstract

We generalize a basis adaptation method for cost approximation in Markov decision processes (MDP), extending earlier work of Menache, Mannor, and Shimkin. In our context, basis functions are parametrized and their parameters are tuned by minimizing an objective function involving the cost function approximation obtained when a temporal differences (TD) or other method is used. The adaptation scheme involves only low order calculations and can be implemented in a way analogous to policy gradient methods. In the generalized basis adaptation framework we provide extensions to TD methods for nonlinear optimal stopping problems and to alternative cost approximations beyond those based on TD.

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