SHS Web Conf.
Volume 144, 20222022 International Conference on Science and Technology Ethics and Human Future (STEHF 2022)
|Number of page(s)||4|
|Section||Application of Artificial Intelligence Technology and Machine Learning Algorithms|
|Published online||26 August 2022|
- V. Mnih, K. Kavukcuoglu, D. Silver, et al., Playing atari with deep reinforcement learning, arXiv preprint arXiv:1312.5602, 2013. [Google Scholar]
- N. Appiah and S. Vare, Playing flappybird with deep reinforcement learning, 2018. [Google Scholar]
- A. Brandao, P. Pires, and P. Georgieva, Reinforcement learning and neuroevolution in flappy bird game, in Iberian Conference on Pattern Recognition and Image Analysis, Springer, 2019, pp. 225-236. [Google Scholar]
- K. Shao, Z. Tang, Y. Zhu, N. Li, and D. Zhao, A survey of deep reinforcement learning in video games, arXiv preprint arXiv:1912.10944, 2019. [Google Scholar]
- K. Chen, Deep reinforcement learning for flappy bird, 2015. [Google Scholar]
- J. Fan, Z. Wang, Y. Xie, and Z. Yang, A theoretical analysis of deep q-learning, in Learning for Dynamics and Control, PMLR, 2020, pp. 486-489. [Google Scholar]
- L. S. Pilcer, A. Hoorelbeke, and A. Andigne, Playing flappy bird with deep reinforcement learning [c] IEEE Transactions on Neural Networks, vol. 16, no. 1, pp. 285-286, 2015. [Google Scholar]
- T. Hester, M. Vecerik, O. Pietquin, et al., Deep qlearning from demonstrations, in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, 2018. [CrossRef] [Google Scholar]
- A. Ganesh, J. Charalel, M. D. Sarma, and N. Xu, Deep reinforcement learning for simulated autonomous driving, 2016. [Google Scholar]
- T. Vu and L. Tran, Flapai bird: Training an agent to play flappy bird using reinforcement learning techniques, arXiv preprint arXiv:2003.09579, 2020. [Google Scholar]
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