Asynchronous methods for deep reinforcement learning V Mnih, AP Badia, M Mirza, A Graves, T Lillicrap, T Harley, D Silver, ... International conference on machine learning, 1928-1937 , 2016 Deep reinforcement learning agorithms used in the Atari series of games, inlcuding Deep Q Network (DQN) algorithm , 51-atom-agent (C51) algorithm , and those suitable for continuous fieds with low search depth and narrow decision tree width [7–15], have achieved or exceeded the level of human experts. In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL). Verified email at google.com. 2016 Understanding Convolutional Neural Networks[J] Google Scholar. For example, a reinforcement learning system playing a video game learns to seek rewards (find some treasure) and avoid punishments (lose money). Planning-based approaches achieve far higher scores than the best model-free approaches, but they exploit information that is not available to human players, and they are orders of magnitude slower than needed for real-time play. V Mnih, K Kavukcuoglu, D Silver, A Graves, I Antonoglou, D Wierstra, ... JT Springenberg, A Dosovitskiy, T Brox, M Riedmiller, D Silver, G Lever, N Heess, T Degris, D Wierstra, M Riedmiller, European Conference on Machine Learning, 317-328, Computer Standards & Interfaces 16 (3), 265-278, A Eitel, JT Springenberg, L Spinello, M Riedmiller, W Burgard, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems …, A Dosovitskiy, JT Springenberg, M Riedmiller, T Brox, Advances in neural information processing systems, 766-774, In Proceedings of the Seventeenth International Conference on Machine Learning. Playing Atari With Deep Reinforcement Learning. Asynchronous methods for deep reinforcement learning V Mnih, AP Badia, M Mirza, A Graves, T Lillicrap, T Harley, D Silver, ... International conference on machine learning, 1928-1937 , 2016 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. M Vecerik, T Hester, J Scholz, F Wang, O Pietquin, B Piot, N Heess, ... J Schneider, WK Wong, A Moore, M Riedmiller, New articles related to this author's research, Human-level control through deep reinforcement learning, A direct adaptive method for faster backpropagation learning: The RPROP algorithm, Playing atari with deep reinforcement learning, Striving for simplicity: The all convolutional net, Neural fitted Q iteration–first experiences with a data efficient neural reinforcement learning method, Advanced supervised learning in multi-layer perceptrons—from backpropagation to adaptive learning algorithms, Multimodal deep learning for robust RGB-D object recognition, Discriminative unsupervised feature learning with convolutional neural networks, An algorithm for distributed reinforcement learning in cooperative multi-agent systems, Emergence of locomotion behaviours in rich environments, Embed to control: A locally linear latent dynamics model for control from raw images, Rprop-description and implementation details, Discriminative unsupervised feature learning with exemplar convolutional neural networks, Deep auto-encoder neural networks in reinforcement learning, A learned feature descriptor for object recognition in rgb-d data, Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards. )cite arxiv:1312.5602Comment: NIPS Deep Learning Workshop 2013. 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. Recently, tremendous success in artificial intelligence has been achieved across different disciplines 16-27 including radiation oncology. Koushik J. Model-free reinforcement learning (RL) can be used to learn effective policies for complex tasks, such as Atari games, even from image observations. We apply our method to seven Atari 2600 games from the Arcade Learning Environment, with no adjustment of the architecture or learning algorithm. , books, abstracts and court opinions disciplines 16-27 including radiation oncology, with adjustment. ) cite arxiv:1312.5602Comment: NIPS deep learning model to successfully learn control policies from... With asynchronous off-policy updates the first deep learning model to successfully learn control policies from. 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