Quantile Reinforcement Learning

Hugo Gilbert; Paul Weng

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arXiv · 1611.00862

Quantile Reinforcement Learning

Hugo Gilbert·Paul Weng

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Abstract

In reinforcement learning, the standard criterion to evaluate policies in a state is the expectation of (discounted) sum of rewards. However, this criterion may not always be suitable, we consider an alternative criterion based on the notion of quantiles. In the case of episodic reinforcement learning problems, we propose an algorithm based on stochastic approximation with two timescales. We evaluate our proposition on a simple model of the TV show, Who wants to be a millionaire.

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Hugo Gilbert, Paul Weng. 2016-11-03. Quantile Reinforcement Learning. https://arxiv.org/abs/1611.00862

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