Transcription of Deep Reinforcement Learning with Double Q-learning
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Deep Reinforcement Learning with Double Q-learning
Deep Reinforcement Learning with Double Q-learningHado van HasseltandArthur GuezandDavid SilverGoogle DeepMindAbstractThe popular Q- Learning algorithm is known to overestimateaction values under certain conditions. It was not previouslyknown whether, in practice, such overestimations are com-mon, whether they harm performance, and whether they cangenerally be prevented. In this paper, we answer all thesequestions affirmatively. In particular, we first show that therecent DQN algorithm, which combines Q- Learning with adeep neural network, suffers from substantial overestimationsin some games in the Atari 2600 domain. We then show thatthe idea behind the Double Q- Learning algorithm, which wasintroduced in a tabular setting, can be generalized to workwith large-scale function approximation.
Deep Reinforcement Learning with Double Q-learning Hado van Hasselt and Arthur Guez and David Silver Google DeepMind Abstract The popular Q-learning algorithm is …
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