Prioritized Experience Replay. - Google Scholar Metrics

Articles citing Prioritized Experience Replay.

Title / Author	Year
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
Dueling network architectures for deep reinforcement learning Z Wang, T Schaul, M Hessel, H Hasselt, M Lanctot, N Freitas International conference on machine learning, 1995-2003	2016
Building machines that learn and think like people BM Lake, TD Ullman, JB Tenenbaum, SJ Gershman Behavioral and brain sciences 40	2017
Hindsight experience replay M Andrychowicz, F Wolski, A Ray, J Schneider, R Fong, P Welinder, ... Advances in neural information processing systems, 5048-5058	2017
Rainbow: Combining improvements in deep reinforcement learning M Hessel, J Modayil, H Van Hasselt, T Schaul, G Ostrovski, W Dabney, ... Thirty-Second AAAI Conference on Artificial Intelligence	2018
Reinforcement learning with unsupervised auxiliary tasks M Jaderberg, V Mnih, WM Czarnecki, T Schaul, JZ Leibo, D Silver, ... arXiv preprint arXiv:1611.05397	2016
Continuous deep q-learning with model-based acceleration S Gu, T Lillicrap, I Sutskever, S Levine International Conference on Machine Learning, 2829-2838	2016
Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation TD Kulkarni, K Narasimhan, A Saeedi, J Tenenbaum Advances in neural information processing systems, 3675-3683	2016
Deep exploration via bootstrapped DQN I Osband, C Blundell, A Pritzel, B Van Roy Advances in neural information processing systems, 4026-4034	2016
Deep reinforcement learning: An overview Y Li arXiv preprint arXiv:1701.07274	2017
Deep reinforcement learning: A brief survey K Arulkumaran, MP Deisenroth, M Brundage, AA Bharath IEEE Signal Processing Magazine 34 (6), 26-38	2017
Addressing function approximation error in actor-critic methods S Fujimoto, H Van Hoof, D Meger arXiv preprint arXiv:1802.09477	2018
A distributional perspective on reinforcement learning MG Bellemare, W Dabney, R Munos arXiv preprint arXiv:1707.06887	2017
Sample efficient actor-critic with experience replay Z Wang, V Bapst, N Heess, V Mnih, R Munos, K Kavukcuoglu, ... arXiv preprint arXiv:1611.01224	2016
State-of-the-art deep learning: Evolving machine intelligence toward tomorrow’s intelligent network traffic control systems ZM Fadlullah, F Tang, B Mao, N Kato, O Akashi, T Inoue, K Mizutani IEEE Communications Surveys & Tutorials 19 (4), 2432-2455	2017
Deep learning in mobile and wireless networking: A survey C Zhang, P Patras, H Haddadi IEEE Communications Surveys & Tutorials 21 (3), 2224-2287	2019
A brief survey of deep reinforcement learning K Arulkumaran, MP Deisenroth, M Brundage, AA Bharath arXiv preprint arXiv:1708.05866	2017
Playing FPS games with deep reinforcement learning G Lample, DS Chaplot Thirty-First AAAI Conference on Artificial Intelligence	2017
Deep q-learning from demonstrations T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, D Horgan, ... Thirty-Second AAAI Conference on Artificial Intelligence	2018
Safe and efficient off-policy reinforcement learning R Munos, T Stepleton, A Harutyunyan, M Bellemare Advances in Neural Information Processing Systems, 1054-1062	2016

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