Transcription of Asynchronous Methods for Deep Reinforcement Learning
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Asynchronous Methods for Deep Reinforcement Learning
Asynchronous Methods for deep Reinforcement LearningVolodymyr Puigdom nech P. DeepMind2 Montreal Institute for Learning Algorithms (MILA), University of MontrealAbstractWeproposeaconceptuallysi mpleandlightweight framework for deep reinforce-ment Learning that uses Asynchronous gradientdescent for optimization of deep neural networkcontrollers. We present Asynchronous variants offour standard Reinforcement Learning algorithmsand show that parallel actor-learners have astabilizing effect on training allowing all fourmethods to successfully train neural best performing method, anasynchronous variant of actor-critic, surpassesthe current state-of-the-art on the Atari domainwhile training for half the time on a singlemulti-core CPU instead of a GPU. Furthermore,we show that Asynchronous actor-critic succeedson a wide variety of continuous motor controlproblems as well as on a new task of navigatingrandom 3D mazes using a visual IntroductionDeep neural networks provide rich representations that canenable Reinforcement Learning (RL) algorithms to performeffectively.
Asynchronous Methods for Deep Reinforcement Learning time than previous GPU-based algorithms, using far less resource than massively distributed approaches. The best of the proposed methods, asynchronous advantage actor-critic (A3C), also mastered a variety of continuous motor control tasks as well as learned general strategies for ex-
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