Munchausen Reinforcement Learning

NIPS 2020, 2020.

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We extended the Munchausen idea to distributional Reinforcement Learning, showing that it could be successfully combined with Implicit Quantile Network to outperform the Rainbow baseline

Abstract:

Bootstrapping is a core mechanism in Reinforcement Learning (RL). Most algorithms, based on temporal differences, replace the true value of a transiting state by their current estimate of this value. Yet, another estimate could be leveraged to bootstrap RL: the current policy. Our core contribution stands in a very simple idea: adding t...More

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