Playing Atari with Deep Reinforcement Learning (Paper Explained)

Playing Atari with Deep Reinforcement Learning (Paper Explained)

Jan 30, 2021
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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. Authors: Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin Riedmiller (DeepMind)

0:00 - Intro & Overview 2:50 - Arcade Learning Environment 4:25 - Deep Reinforcement Learning 9:20 - Deep Q-Learning 26:30 - Experience Replay 32:25 - Network Architecture 33:50 - Experiments 37:45 - Conclusion
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