An Adiabatic Theorem for Policy Tracking with TD-learning

Neil Walton

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arXiv · 2010.12848

An Adiabatic Theorem for Policy Tracking with TD-learning

Neil Walton

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Abstract

We evaluate the ability of temporal difference learning to track the reward function of a policy as it changes over time. Our results apply a new adiabatic theorem that bounds the mixing time of time-inhomogeneous Markov chains. We derive finite-time bounds for tabular temporal difference learning and $Q$-learning when the policy used for training changes in time. To achieve this, we develop bounds for stochastic approximation under asynchronous adiabatic updates.

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Neil Walton. 2020-10-24. An Adiabatic Theorem for Policy Tracking with TD-learning. https://arxiv.org/abs/2010.12848

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