#ai #dqn #deepmind
After the initial success of deep neural networks, especially convolutional neural networks on supervised image processing tasks, this paper was the first to demonstrate their applicability to reinforcement learning. Deep Q Netwo…
#ai #dqn #deepmind
After the initial success of deep neural networks, especially convolutional neural networks on supervised image processing tasks, this paper was the first to demonstrate their applicability to reinforcement learning. Deep Q Networks learn from pixel input to play seven different Atari games and outperform baselines that require hand-crafted features. This paper kicked off the entire field of deep reinforcement learning and positioned DeepMind as one of the leading AI companies in the world.
OUTLINE:
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
Paper: https://arxiv.org/abs/1312.5602
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
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