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.
and the experience replay dramatically improve the perfor-mance of the algorithm (Mnih et al., 2015). Double Q-learning The max operator in standard Q-learning and DQN, in (2) and (3), uses the same values both to select and to evalu-ate an action. This makes it more likely to select overesti-mated values, resulting in overoptimistic value ...
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