Asynchronous Methods for Deep Reinforcement Learning

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Authors Volodymyr Mnih, Mehdi Mirza, Alex Graves, David Silver, Tim Harley, Timothy P. Lillicrap, Adrià Puigdomènech Badia, Koray Kavukcuoglu
Journal/Conference Name 33rd International Conference on Machine Learning, ICML 2016
Paper Category
Paper Abstract We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers. We present asynchronous variants of four standard reinforcement learning algorithms and show that parallel actor-learners have a stabilizing effect on training allowing all four methods to successfully train neural network controllers. The best performing method, an asynchronous variant of actor-critic, surpasses the current state-of-the-art on the Atari domain while training for half the time on a single multi-core CPU instead of a GPU. Furthermore, we show that asynchronous actor-critic succeeds on a wide variety of continuous motor control problems as well as on a new task of navigating random 3D mazes using a visual input.
Date of publication 2016
Code Programming Language Multiple
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