Abstract
In this article, we propose a method to adapt stepsize parameters used in reinforcement learning for non-stationary environments. When the environment is non-stationary, the learning agent must adapt learning parameters like stepsize to the changes of environment through continuous learning. We show several theorems on higher-order derivatives of exponential moving average, which is a base schema of major reinforcement learning methods, using stepsize parameters. We also derive a systematic mechanism to calculate these derivatives in a recursive manner. Based on it, we construct a precise and flexible adaptation method for the stepsize parameter in order to maximize a certain criterion. The proposed method is also validated by several experimental results.
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Noda, I. (2009). Recursive Adaptation of Stepsize Parameter for Non-stationary Environments. In: Yang, JJ., Yokoo, M., Ito, T., Jin, Z., Scerri, P. (eds) Principles of Practice in Multi-Agent Systems. PRIMA 2009. Lecture Notes in Computer Science(), vol 5925. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11161-7_38
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DOI: https://doi.org/10.1007/978-3-642-11161-7_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11160-0
Online ISBN: 978-3-642-11161-7
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