참고문헌
- S. Jeong and M. Won, "Motion Planning and Control of Wheel-legged Robot for Obstacle Crossing," The Journal of Korea Robotics Society, vol.17, no.4, pp. 500-507, Dec., 2022, DOI: 10.7746/jkros.2022.17.4.500.
- X. B. P eng, G. Berseth, K. Yin, and M. Van De P anne, "DeepLoco: Dynamic locomotion skills using hierarchical deep reinforcement learning," ACM Transactions on Graphics, vol. 36, no. 4, pp. 1-13, Jul., 2017, DOI: 10.1145/3072959.3073602.
- V. Tsounis, M. Alge, J. Lee, F. Farshidian, and M. Hutter, "DeepGait: Planning and control of quadrupedal gaits using deep reinforcement learning," IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 3699-3706, Apr., 2020, DOI: 10.1109/LRA.2020.2979660.
- N. Rudin, D. Hoeller, P. Reist, and M. Hutter, "Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning," Robotics, 2021, DOI: 10.48550/arXiv.2109.11978.
- K. Arulkumaran, M. P . Deisenroth, M. Brundage, and A. A. Bharath, "Deep reinforcement learning: a brief survey," IEEE Signal Processing Magazine, vol. 34, no. 6, pp. 26-38, Nov., 2017, DOI: 10.1109/MSP.2017.2743240.
- M. Bain and C. Sammut, "A framework for behavioural cloning," Machine Intelligence 15, 1995, [Online], https://www.semanticscholar.org/paper/A-Framework-for-Behavioural-Cloning-Bain-Sammut/1f4731d5133cb96ab30e08bf39dffa874aebf487, Accessed: Feb., 13, 2023.
- J. D. Chang, M. Uehara, D. Sreenivas, R. Kidambi, and W. Sun, "Mitigating covariate shift in imitation learning via offline data with partial coverage," Machine Learning, 2021, DOI: 10.48550/arXiv.2106.03207.
- J. Peters and S. Schaal, "Reinforcement learning of motor skills with policy gradients," Neural networks, vol. 21, no. 4, pp. 682-697, May, 2008, DOI: 10.1016/j.neunet.2008.02.003.
- M. S. Jazayeri and A. Jahangiri, "Utilizing b-spline curves and neural networks for vehicle trajectory prediction in an inverse reinforcement learning framework," Journal of Sensor and Actuator Networks, vol. 11, no. 1, Feb., 2022, DOI: 10.3390/jsan11010014.
- M. Hasanzade and E. Koyuncu, "A dynamically feasible fast replanning strategy with deep reinforcement learning," Journal of Intelligent & Robotic Systems, vol. 101, 2021, DOI: 10.1007/s10846-020-01274-1.
- E. Todorov, T. Erez, and Y. Tassa, "MuJoCo: A physics engine for model-based control," IEEE International Workshop on Intelligent Robots and Systems (IROS), Vilamoura-Algarve, Portugal, 2012, DOI: 10.1109/IROS.2012.6386109.
- pybind11 - Seamless operability between C++11 and Python, [Online], https://github.com/pybind/pybind11, Accessed: Nov. 1, 2022.
- Eigen is a C++ template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms, [Online], https://eigen.tuxfamily.org, Accessed: Nov. 1, 2022.