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Implicit Quantile Networks for Distributional Reinforcement Learning
Will Dabney · Georg Ostrovski · David Silver · Remi Munos

Wed Jul 11 09:15 AM -- 12:00 PM (PDT) @ Hall B #3

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.

Author Information

Will Dabney (DeepMind)
Georg Ostrovski (DeepMind)
David Silver (Google DeepMind)
Remi Munos (DeepMind)

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