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arXiv · 1903.01004

Budgeted Reinforcement Learning in Continuous State Space

Nicolas Carrara·Edouard Leurent·Romain Laroche·Tanguy Urvoy·Odalric-Ambrym Maillard·Olivier Pietquin
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Abstract

A Budgeted Markov Decision Process (BMDP) is an extension of a Markov Decision Process to critical applications requiring safety constraints. It relies on a notion of risk implemented in the shape of a cost signal constrained to lie below an - adjustable - threshold. So far, BMDPs could only be solved in the case of finite state spaces with known dynamics. This work extends the state-of-the-art to continuous spaces environments and unknown dynamics. We show that the solution to a BMDP is a fixed point of a novel Budgeted Bellman Optimality operator. This observation allows us to introduce natural extensions of Deep Reinforcement Learning algorithms to address large-scale BMDPs. We validate our approach on two simulated applications: spoken dialogue and autonomous driving.

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Nicolas Carrara, Edouard Leurent, Romain Laroche, Tanguy Urvoy, Odalric-Ambrym Maillard, Olivier Pietquin. 2019-03-03. Budgeted Reinforcement Learning in Continuous State Space. https://arxiv.org/abs/1903.01004

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