We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them. Volodymyr Mnih, et al., "Playing Atari with Deep Reinforcement Learning" https://arxiv.org/abs/1312.5602 強化学習にディ…

We apply our method to seven Atari 2600 games from the Arcade Learning Environment, with no adjustment of the architecture or learning algorithm. Volodymyr Mnih, et al., "Playing Atari with Deep Reinforcement Learning" https://arxiv.org/ab…

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. Volodymyr Mnih, et al., "Playing Atari with Deep Reinforcement Lea…

We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. Volodymyr Mnih, et al., "Playing Atari with Deep Reinforcement Learning" https://arx…