Fetching the paper…
Reading the bibliography…
This paper was motivated by the problem of how to make robots fuse and transfer their experience so that they can effectively use prior knowledge and quickly adapt to new environments.
V. Mnih et al., “Asynchronous methods for deep reinforcement learning,” in International conference on machine learning (ICML), 2016, pp. 1928-1937
1937
Earlier work this paper cites.
J.Kuffner, “Cloud-enabled Robots,” in International Conference on Humanoid Robot, 2010
2010
Earlier work this paper cites.
M. Waibel et al., “RoboEarth - A World Wide Web for Robots,” IEEE Robotics and Automation Magazine, vol. 18, no. 2, pp. 69-82, 2011
2011
Earlier work this paper cites.
M. Srinivasan, K. Sarukesi, N. Ravi, and M. T, “Design and Implementation of VOD (Video on Demand) SaaS Framework for Android Platform on Cloud Environment,” in IEEE International Conference on Mobile Data Management, 2013, pp. 171-176
2013
Earlier work this paper cites.
H. B. Ammar, E. Eaton, P. Ruvolo, and M. E. Taylor, “Online Multi-Task Learning for Policy Gradient Methods,” in International Conference on Machine Learning (ICML), 2014, pp. 1206-1214
2014
Earlier work this paper cites.
E. Brunskill and L. Li, “PAC-inspired Option Discovery in Lifelong Reinforcement Learning,” International Conference on Machine Learning (ICML), pp. 316-324, 2014
2014
Earlier work this paper cites.
B. Kehoe, S. Patil, P. Abbeel, and K. Goldberg, “A Survey of Research on Cloud Robotics and Automation,” IEEE Transactions on Automation Science and Engineering, vol. 12, no. 2, pp. 398-409, 2015
2015
Earlier work this paper cites.
T. Schaul, D. Horgan, K. Gregor, and D. Silver, “Universal Value Function Approximators,” in International Conference on Machine Learning (ICML), 2015, pp. 1312-1320
2015
Earlier work this paper cites.
V. Mnih et al., “Human-level control through deep reinforcement learning,” Nature, vol. 518, no. 7540, pp. 529-533, 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
A. A. Rusu et al., “Policy Distillation,” in International Conference on Learning Representations (ICLR), 2016
2016
Cited alongside, same era.
A. A. Rusu et al., “Progressive Neural Networks,” in Conference and Workshop on Neural Information Processing Systems (NIPS), 2016
2016
Cited alongside, same era.
2016
Cited alongside, same era.
M. Jaderberg et al., “Reinforcement learning with unsupervised auxiliary tasks,” in International Conference on Learning Representations (ICLR), 2017
2017
Cited alongside, same era.
Y. Zhu et al., “Target-driven visual navigation in indoor scenes using deep reinforcement learning,” in IEEE International Conference on Robotics and Automation (ICRA), 2017, pp. 3357-3364
C. Tessler, S. Givony, T. Zahavy, D. J. Mankowitz, and S. Mannor, “A Deep Hierarchical Approach to Lifelong Learning in Minecraft.,” in AAAI, 2017, vol. 3, pp. 1553-1561
2017
Later among the works it cites.
Y. Li, “Deep Reinforcement Learning,” arXiv preprint arXiv:1810.16339, 2018
2018
Later among the works it cites.
P. Long, T. Fanl, X. Liao, W. Liu, H. Zhang, and J. Pan, “Towards optimally decentralized multi-robot collision avoidance via deep reinforcement learning,” in IEEE International Conference on Robotics and Automation (ICRA), 2018, pp. 6252-6259
2018
Later among the works it cites.
B. Kiumarsi, K. G. Vamvoudakis, H. Modares, and F. L. Lewis, “Optimal and Autonomous Control Using Reinforcement Learning: A Survey,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 6, pp. 2042-2062, 2018
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
J. Zhang, J. T. Springenberg, J. Boedecker, and W. Burgard, “Deep reinforcement learning with successor features for navigation across similar environments,” in IEEE International Conference on Intelligent Robots and Systems (IROS), 2017, pp. 2371-2378
2017
Cited alongside, same era.
P. Mirowski et al., “Learning to Navigate in Complex Environments,” in International Conference on Learning Representations (ICLR), 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
L. Tai, G. Paolo, and M. Liu, “Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation,” in IEEE International Conference on Intelligent Robots and Systems (IROS), 2017, pp. 31-36
2017
Cited alongside, same era.
H. Xu, Y. Gao, F. Yu, and T. Darrell, “End-to-end learning of driving models from large-scale video datasets,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 2174-2182
2017
Cited alongside, same era.
Z. Chen, B. Liu, R. Brachman, P. Stone, and F. Rossi, “Lifelong Machine Learning: Second Edition,” 2nd ed. Morgan and Claypool, 2018
2018
Later among the works it cites.
M. Nasr, R. Shokri, and A. Houmansadr, “Comprehensive Privacy Analysis of Deep Learning: Stand-alone and Federated Learning under Passive and Active White-box Inference Attacks,” in IEEE Symposium on Security and Privacy, 2018, pp. 1-15
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
Y. Tsurumine, Y. Cui, E. Uchibe, and T. Matsubara, “Deep reinforcement learning with smooth policy update: Application to robotic cloth manipulation,” Robotics and Autonomous Systems, vol. 112, pp. 72-83, 2019
2019
Closest in time.