In this work, we build on recent advances in distributional reinforcement learning to give a generally applicable, flexible, and state-of-the-art distributional variant of DQN. We achieve this by using quantile regression to approximate the full quantile function for the state-action return distribution. By reparameterizing a distribution over the sample space, this yields an implicitly defined return distribution and gives rise to a large class of risk-sensitive policies. We demonstrate improved performance on the 57 Atari 2600 games in the ALE, and use our algorithm's implicitly defined distributions to study the effects of risk-sensitive policies in Atari games.
@article{arxiv.1806.06923,
title = {Implicit Quantile Networks for Distributional Reinforcement Learning},
author = {Will Dabney and Georg Ostrovski and David Silver and Rémi Munos},
journal= {arXiv preprint arXiv:1806.06923},
year = {2018}
}