Continuous control with deep reinforcement learning.

Articles citing Continuous control with deep reinforcement learning.

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
End-to-end training of deep visuomotor policies S Levine, C Finn, T Darrell, P Abbeel The Journal of Machine Learning Research 17 (1), 1334-1373	2016
Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection S Levine, P Pastor, A Krizhevsky, J Ibarz, D Quillen The International Journal of Robotics Research 37 (4-5), 421-436	2018
High-dimensional continuous control using generalized advantage estimation J Schulman, P Moritz, S Levine, M Jordan, P Abbeel arXiv preprint arXiv:1506.02438	2015
Benchmarking deep reinforcement learning for continuous control Y Duan, X Chen, R Houthooft, J Schulman, P Abbeel International Conference on Machine Learning, 1329-1338	2016
Weight normalization: A simple reparameterization to accelerate training of deep neural networks T Salimans, DP Kingma Advances in neural information processing systems, 901-909	2016
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor T Haarnoja, A Zhou, P Abbeel, S Levine arXiv preprint arXiv:1801.01290	2018
Curiosity-driven exploration by self-supervised prediction D Pathak, P Agrawal, AA Efros, T Darrell Proceedings of the IEEE Conference on Computer Vision and Pattern …	2017
Uncertainty in deep learning Y Gal University of Cambridge 1 (3)	2016
Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates S Gu, E Holly, T Lillicrap, S Levine 2017 IEEE international conference on robotics and automation (ICRA), 3389-3396	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
Deep reinforcement learning that matters P Henderson, R Islam, P Bachman, J Pineau, D Precup, D Meger Thirty-Second AAAI Conference on Artificial Intelligence	2018
Designing neural network architectures using reinforcement learning B Baker, O Gupta, N Naik, R Raskar arXiv preprint arXiv:1611.02167	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
Robust physical-world attacks on deep learning visual classification K Eykholt, I Evtimov, E Fernandes, B Li, A Rahmati, C Xiao, A Prakash, ... Proceedings of the IEEE Conference on Computer Vision and Pattern …	2018
The marginal value of adaptive gradient methods in machine learning AC Wilson, R Roelofs, M Stern, N Srebro, B Recht Advances in neural information processing systems, 4148-4158	2017
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
Emergence of locomotion behaviours in rich environments N Heess, D TB, S Sriram, J Lemmon, J Merel, G Wayne, Y Tassa, T Erez, ... arXiv preprint arXiv:1707.02286	2017

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