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The Value Function Polytope in Reinforcement Learning

Adrien Ali Taiga
Nicolas Le Roux
Dale Schuurmans
Marc G. Bellemare
Proceedings of the 36th International Conference on Machine Learning, ICML (2019), pp. 1486-1495 (to appear)

Abstract

We establish geometric and topological properties of the space of value functions in finite state-action Markov decision processes. Our main contribution is the characterization of the nature of its shape: a general polytope \cite{aigner2010proofs}. To demonstrate this result, we exhibit several properties of the structural relationship between policies and value functions including the line theorem, which shows that the value functions of policies constrained on all but one state describe a line segment. Finally, we use this novel perspective and introduce visualizations to enhance the understanding of the dynamics of reinforcement learning algorithms.

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