# Quantile Reinforcement Learning

arXiv: https://arxiv.org/abs/1611.00862 · HTML: https://mlsanity.com/paper/1611.00862/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2016.

## Built on by (most cited first, 2 of 2)

- [An Analysis of Quantile Temporal-Difference Learning](https://mlsanity.com/paper/2301.04462/) (2023)
- [Q-learning for Quantile MDPs: A Decomposition, Performance, and Convergence Analysis](https://mlsanity.com/paper/2410.24128/) (2024)

## Abstract (arXiv)

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.