Acknowledgement
This project was funded by Sogang University Research & Business Development Foundation
References
- N. Kohl and P. Stone, "Policy Gradient Reinforcement Learning for Fast Quadrupedal Locomotion," 2004 IEEE International Conference on Robotics & Automation, New Orleans, LA, USA, pp. 2619-2624, 2004. DOI: 10.1109/ROBOT.2004.1307456.
- M. T. Rosenstein and A. G. Barto, "Robot Weightlifting By Direct Policy Search," 2001 International Joint Conference on Artificial Intelligence, Seattle, USA, pp. 839-844, 2001, [Online], https://dl.acm.org/doi/abs/10.5555/1642194.1642206.
- J. Kober and J. Peters, "Policy Search for Motor Primitives in Robotics," Machine Learning, vol. 84, pp. 171-203, 2011, DOI: 10.1007/s10994-010-5223-6.
- P. Kormushev, S. Calinon, R. Saegusa, and G. Metta, "Learning the skill of archery by a humanoid robot iCub," 2010 10th IEEE-RAS International Conference on Humanoid Robots, Nashville, TN, USA, pp. 417-423, 2010, DOI: 10.1109/ICHR.2010.5686841.
- D. H. Kang, J. H. Bong, J. Park, and S. Park, "Reinforcement Learning Strategy for Automatic Control of Real-time Obstacle Avoidance based on Vehicle Dynamics," Journal of Korea Robotics Society, vol. 12, no. 3, pp. 297-305, Sept., 2017, DOI: 10.7746/jkros.2017.12.3.297.
- R. S. Sutton and A. G. Barto, "Introduction," Reinforcement Learning: An Introduction, 2nd ed. The MIT Press, 2014, ch. 1, sec. 1-7, pp.1-18, [Online], https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf.
- M. P. Deisenroth, G. Neumann, and J. Peters, "A Survey on Policy Search for Robotics," Foundation and Trends® in Robotics, vol. 2, no. 1-2, pp. 1-142, 2013, DOI: 10.1561/2300000021.
- Y. H. Yang, S. H. Lee, and C. S. Lee, "Designing an Efficient Reward Function for Robot Reinforcement Learning of The Water Bottle Flipping Task," Journal of Korea Robotics Society, vol. 14, no. 2, pp. 81-86, Jun., 2019, DOI: 10.7746/jkros.2019.14.2.081.
- P. Abbeel, M. Quigley, and A. Y. Ng, "Using Inaccurate Models in Reinforcement Learning," 23rd International Conference on Machine Learning, Pittsburgh, Pennsylvania, USA, pp. 1-8, 2006, DOI: 10.1145/1143844.1143845.
- M. J. Mataric, "Reward Functions for Accelerated Learning," Eleventh International Conference, Brunswick, NJ, USA, pp. 181-189, 1994, DOI: 10.1016/B978-1-55860-335-6.50030-1.
- H. Hachiya, J. Peters, and M. Sugiyama, "Reward-Weighted Regression with Sample Reuse for Direct Policy Search in Reinforcement Learning," Neural Computation, vol. 23, no. 11, pp. 2798-2832, 2011, DOI: 10.1162/NECO_a_00199.
- B. C. da Silva, G. Baldassarre, G. Konidaris, and A. Barto, "Learning parameterized motor skills on a humanoid robot," 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, pp. 5239-5244, 2014, DOI: 10.1109/ICRA.2014.6907629.
- S. H. Lee, "Designing an efficient reward function for robot reinforcement learning of the water bottle flipping task," M.S thesis, Sogang University, Seoul, Korea, 2018, [Online], https://library.sogang.ac.kr/search/detail/CAT000000843771.
- J. Kober and J. Peters, "Learning Motor Primitives for Robotics," 2009 IEEE International Conference on Robotics and Automation, Kobe, Japan, pp. 2112-2118, 2009, DOI: 10.1109/ROBOT.2009.5152577.
- J. Wang, Y. Liu, and B. Li, "Reinforcement Learning with Perturbed Rewards," AAAI Technical Track: Machine Learning, 2020, DOI: 10.1609/aaai.v34i04.6086.
- K. Framling, "Reinforcement Learning in a Noisy Environment: Light-Seeking Robot," WSEAS Transactions on Systems, vol. 3, no. 2, pp. 714-719, 2004, [Online], https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.484.6001&rep=rep1&type=pdf.