Playing Atari with Deep Reinforcement Learning
Volodymyr Mnih
and
Koray Kavukcuoglu
and
David Silver
and
Alex Graves
and
Ioannis Antonoglou
and
Daan Wierstra
and
Martin Riedmiller
arXiv e-Print archive - 2013 via Local arXiv
Keywords:
cs.LG
First published: 2013/12/19 (10 years ago) Abstract: We present the first deep learning model to successfully learn control
policies directly 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 Learning 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.
