Title: Implicit Quantile Networks for Distributional Reinforcement Learning,
Abstract: 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.