Abstract
We introduce and empirically evaluate two novel online gradient-based reinforcement learning algorithms with function approximation – one model based, and the other model free. These algorithms come with the possibility of having non-squared loss functions which is novel in reinforcement learning, and seems to come with empirical advantages. We further extend a previous gradient based algorithm to the case of full control, by using generalized policy iteration. Theoretical properties of these algorithms are studied in a companion paper.
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Robards, M., Sunehag, P. (2012). Gradient Based Algorithms with Loss Functions and Kernels for Improved On-Policy Control. In: Sanner, S., Hutter, M. (eds) Recent Advances in Reinforcement Learning. EWRL 2011. Lecture Notes in Computer Science(), vol 7188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29946-9_7
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DOI: https://doi.org/10.1007/978-3-642-29946-9_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29945-2
Online ISBN: 978-3-642-29946-9
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