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Computer Science > Machine Learning

arXiv:1901.11275 (cs)
[Submitted on 31 Jan 2019 (v1), last revised 4 Jun 2019 (this version, v2)]

Title:A Theory of Regularized Markov Decision Processes

Authors:Matthieu Geist, Bruno Scherrer, Olivier Pietquin
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Abstract:Many recent successful (deep) reinforcement learning algorithms make use of regularization, generally based on entropy or Kullback-Leibler divergence. We propose a general theory of regularized Markov Decision Processes that generalizes these approaches in two directions: we consider a larger class of regularizers, and we consider the general modified policy iteration approach, encompassing both policy iteration and value iteration. The core building blocks of this theory are a notion of regularized Bellman operator and the Legendre-Fenchel transform, a classical tool of convex optimization. This approach allows for error propagation analyses of general algorithmic schemes of which (possibly variants of) classical algorithms such as Trust Region Policy Optimization, Soft Q-learning, Stochastic Actor Critic or Dynamic Policy Programming are special cases. This also draws connections to proximal convex optimization, especially to Mirror Descent.
Comments: ICML 2019
Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML)
Cite as: arXiv:1901.11275 [cs.LG]
  (or arXiv:1901.11275v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.1901.11275
arXiv-issued DOI via DataCite

Submission history

From: Matthieu Geist [view email]
[v1] Thu, 31 Jan 2019 09:10:08 UTC (29 KB)
[v2] Tue, 4 Jun 2019 07:44:24 UTC (50 KB)
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