References
- Herlihy, David V. Bicycle: the history. Yale University Press, 2004.
- Schwab, A. L., J. P. Meijaard, and J. D.G. Kooijman. "Some recent developments in bicycle dynamics." Proceedings of the 12th World Congress in Mechanism and Machine Science. 2007.
- Meijaard, Jaap P., et al. "Linearized dynamics equations for the balance and steer of a bicycle: a benchmark and review." Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences. Vol. 463. No. 2084. The Royal Society, 2007.
- http://ai2001.ifdef.jp/primer_V2/primer_V2.html
- Basso, Michele, and Giacomo Innocenti. "Lego-bike: A challenging robotic lab project to illustrate rapid prototyping in the mindstorms/simulink integrated platform." Computer Applications in Engineering Education 23.6 (2015): 947-958. https://doi.org/10.1002/cae.21666
- Basso, Michele, Giacomo Innocenti, and Alberto Rosa. "Simulink meets lego: Rapid controller prototyping of a stabilized bicycle model." 52nd IEEE Conference on Decisionand Control. IEEE, 2013.
- Randlov, Jette, and Preben Alstrom. "Learning to Drive a Bicycle Using Reinforcement Learning and Shaping." ICML. Vol. 98. 1998.
- Sutton, Richard S., and Andrew G. Barto. Reinforcement learning: An introduction. Vol.1, No.1, Cambridge: MIT press, 1998.
- Lagoudakis, Michail G., and Ronald Parr. "Model-free least-squares policy iteration." NIPS, Vol.14, 2001.
- Lever, Guy. "Deterministic policy gradient algorithms.", 2014.
- Phyo Htet Kyaw, Dyna-Q based Univector Field Obstacle Avoidance for Fast Mobile Robots, Master, KyungHee University, Korea, Seoul, 2011.
- Kaelbling, Leslie Pack, Michael L. Littman, and Andrew W. Moore. "Reinforcement learning: A survey." Journal of artificial intelligence research 4 (1996): 237-285. https://doi.org/10.1613/jair.301
- Irodova, Marina, and Robert H. Sloan. "Reinforcement Learning and Function Approximation." FLAIRS Conference. 2005.
- Watkins, Christopher JCH, and Peter Dayan. "Q-learning." Machine learning 8.3-4 (1992): 279-292. https://doi.org/10.1007/BF00992698
- G.A. Rummery and M. Niranjan, On-Line Q-Learning Using Connectionist Systems. Technical Report CUED/F-INFENG/TR 166, Cambridge University Engineering Department, 1994.
- Grondman, Ivo, et al. "A survey of actor-critic reinforcement learning: Standard and natural policy gradients." IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 42.6 (2012): 1291-1307. https://doi.org/10.1109/TSMCC.2012.2218595
- Sutton, Richard S., et al. "Policy Gradient Methods for Reinforcement Learning with Function Approximation." NIPS. Vol. 99. 1999.
- Peters, Jan, and Stefan Schaal. "Policy gradient methods for robotics." 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2006.
- Lillicrap, Timothy P., et al. "Continuous control with deep reinforcement learning." arXiv preprint arXiv:1509.02971 (2015).
- Mnih, Volodymyr, et al. "Human-level control through deep reinforcement learning." Nature 518.7540 (2015): 529-533. https://doi.org/10.1038/nature14236
- Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012.
- Kim Tae Hee, Kang Seung Ho, "An Intrusion Detection System based on the Artificial Neural Network for Real Time Detection." Journal of Information and Security. 2018.