Open Access
| Issue |
JNWPU
Volume 39, Number 5, October 2021
|
|
|---|---|---|
| Page(s) | 1057 - 1063 | |
| DOI | https://doi.org/10.1051/jnwpu/20213951057 | |
| Published online | 14 December 2021 | |
- Lumelsky V, Stepanov A. Dynamic path planning for a mobile automaton with limited information on the environment[J]. IEEE Trans on Automatic Control, 1986, 31(11) : 1058–1063 [Google Scholar]
- Mnih V, Kavukcuoglu K, Silver D, et al. Playing atari with deep reinforcement learning[J]. Computer Science, 2013, 12(19) : 5602 [Google Scholar]
- Lillicrap T P, Hunt J, Pritzel A, et al. Continuous control with deep reinforcement learning[J/OL]. (2019-07-05)[2021-10-22]. https://arxiv.org/pdf/1509.02971v6.pdf [Google Scholar]
- Schulman J, Wolski F, Dhariwal P, et al. Proximal policy optimization algorithms[J/OL]. (2017-08-28)[2021-10-22]. https://arxiv.org/pdf/1707.06347.pdf [Google Scholar]
- Mnih V, Badia A P, Mirza M, et al. Asynchronous methods for deep reinforcement learning[C]//International Conference on Machine Learning, 2016: 1928–1937 [Google Scholar]
- Li J, Shi H, Hwang K S. An explainable ensemble feedforward method with Gaussian convolutional filter[J]. Knowledge Based Systems, 2021(225) : 107103 [Google Scholar]
- Van Hasselt H, Guez A, Silver D. Deep reinforcement learning with double q-learning[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2016 [Google Scholar]
- Schaul T, Horgan D, Gregor K, et al. Universal value function approximators[C]//International Conference on Machine learning, 2015: 1312–1320 [Google Scholar]
- Andrychowicz M, Wolski F, Ray A, et al. Hindsight experience replay[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017: 5055–5065 [Google Scholar]
- Hester T, Vecerik M, Pietquin O, et al. Deep q-learning from demonstrations[C]//Thirty-Second AAAI Conference on Artificial Intelligence, 2018 [Google Scholar]
- Vecerík M, Hester T, Scholz J, et al. Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards[J/OL]. (2018-10-08)[2021-10-22]. https://arxiv.org/pdf/1707.08817v1.pdf [Google Scholar]
- Bochkovskiy A, Wang C Y, Liao H. YOLOv4: optimal speed and accuracy of object detection[J/OL]. (2020-04-23)[2021-10-22]. https://arxiv.org/pdf/2004.10934v1.pdf [Google Scholar]
- Wu Y H, Lin S D. A low-cost ethics shaping approach for designing reinforcement learning agents[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2018 [Google Scholar]
- Christiano P F, Leike J, Brown T B, et al. Deep reinforcement learning from human preferences[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, 2017 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.