최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기전기전자학회논문지 = Journal of IKEEE, v.25 no.2, 2021년, pp.337 - 343
선우영민 (Dept. of Smart Robot Convergence and Application Engineering, Pukyong National University) , 이원창 (Dept. of Electronic Engineering, Pukyong National University)
Deep reinforcement learning is an artificial intelligence algorithm that enables learners to select optimal behavior based on raw and, high-dimensional input data. A lot of research using this is being conducted to create an optimal movement path of a mobile robot in an environment in which obstacle...
K. Zhu and T. Zhang, "Deep reinforcement learning based mobile robot navigation: A review," in Tsinghua Science and Technology, vol.26, no.5, pp.674-691, 2021. DOI: 10.26599/TST.2021.9010012.
Kober, Jens, J. Andrew Bagnell, and Jan Peters. "Reinforcement learning in robotics: A survey," The International Journal of Robotics Research, Vol.32, No.11, pp.1238-1274, 2013. DOI: 10.1177/0278364913495721
Otte, Michael W. "A survey of machine learning approaches to robotic path-planning," University of Colorado at Boulder, Boulder 2015. DOI: 10.1109/RISE.2017.8378228
Mnih, Volodymyr, et al. "Playing atari with deep reinforcement learning," arXiv preprint arXiv:1312.5602, 2013.
Van Hasselt, Hado, Arthur Guez, and David Silver. "Deep reinforcement learning with double q-learning," Proceedings of the AAAI Conference on Artificial Intelligence. Vol.30, No.1, 2016.
Wang, Ziyu, et al. "Dueling network architectures for deep reinforcement learning." International conference on machine learning. PMLR, 2016.
Schaul, Tom, et al. "Prioritized experience replay." arXiv preprint arXiv:1511.05952, 2015.
"Webots: robot simulator" https://cyberbotics.com/#features
Kirtas, M., et al. "Deepbots: A Webots-Based Deep Reinforcement Learning Framework for Robotics." IFIP International Conference on Artificial Intelligence Applications and Innovations. Springer, Cham, 2020.
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