Playing Atari with Deep Reinforcement Learning Volodymyr Mnih Koray Kavukcuoglu David Silver Alex Graves Ioannis Antonoglou Daan Wierstra Martin Riedmiller DeepMind Technologies {vlad,koray,david,alex.graves,ioannis,daan,martin.riedmiller} @ deepmind.com Abstract We present the first deep learning model to successfully learn control policies di- rectly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. We apply our method to seven Atari 2600 games from the Arcade Learn- ing Environment, with no adjustment of the architecture or learning algorithm. We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them. 1 Introduction Learning to control agents directly from high-dimensional sensory inputs like vision and speech is one of th
