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Convergence of Least Squares Temporal Difference Methods Under General Conditions

2020-02-26 | |  79 |   34 |   0

Abstract

We consider approximate policy evaluation for finite state and action Markov decision processes (MDP) in the off-policy learning context and with the simulation-based least squares temporal difference algorithm, LSTD(?). We establish for the discounted cost criterion that the off-policy LSTD(?) converges almost surely under mild, minimal conditions. We also analyze other convergence and boundedness properties of the iterates involved in the algorithm, and based on them, we suggest a modification in its practical implementation. Our analysis uses theories of both finite space Markov chains and Markov chains on topological spaces.

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