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. We propose a spe-cific adaptation to the DQN algorithm and show that the re-sulting algorithm not only reduces the observed overestima-tions, as hypothesized, but that this also leads to much betterperformance on several goal of Reinforcement Learning (Sutton and Barto, 1998)is to learn good policies for sequential decision problems,by optimizing a cumulative future reward signal.
Deep Q Networks A deep Q network (DQN) is a multi-layered neural network that for a given state soutputs a vector of action values Q(s;; ), where are the parameters of the network. For an n-dimensional state space and an action space contain-ing mactions, the neural network is a function from Rnto Rm. Two important ingredients of the DQN ...
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