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This paper investigates exploration strategies of Deep Reinforcement Learning (DRL) methods to learn navigation policies for mobile robots.
V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu, “Asynchronous methods for deep reinforcement learning,” in International Conference on Machine Learning , 2016, pp. 1928–1937
1937
Earlier work this paper cites.
S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics . MIT Press, 2005
2005
Earlier work this paper cites.
Y. Sun, M. Liu, and M. Q.-H. Meng, “WiFi signal strength-based robot indoor localization,” in Information and Automation (ICIA), 2014 IEEE International Conference on . IEEE, 2014, pp. 250–256
2014
Earlier work this paper cites.
2016
Earlier work this paper cites.
Y. Zhu, R. Mottaghi, E. Kolve, J. J. Lim, A. Gupta, L. Fei-Fei, and A. Farhadi, “Target-driven visual navigation in indoor scenes using deep reinforcement learning,” in Robotics and Automation (ICRA), 2017 IEEE International Conference on . IEEE, 2017, pp. 3357–3364
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 Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International Conference on . IEEE, 2017, pp. 2371–2378
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Later among the works it cites.
L. Tai, G. Paolo, and M. Liu, “Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation,” in Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International Conference on . IEEE, 2017, pp. 31–36
2017
Later among the works it cites.
D. Pathak, P. Agrawal, A. A. Efros, and T. Darrell, “Curiosity-driven exploration by self-supervised prediction,” in International Conference on Machine Learning (ICML) , vol. 2017, 2017
2017
Later among the works it cites.
2018
Closest in time.
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