Chris Pollett > Students > Garg
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CS298 ProposalVirtual Robot Climbing using Reinforcement LearningUjjawal Garg (ujjawal.garg@sjsu.edu) Advisor: Dr. Chris Pollett Committee Members: Dr. Katerina Potika, Dr. Robert Chun Abstract:Reinforcement Learning is a field of Artificial Intelligence that has gained a lot of attraction in recent years. Unlike supervised learning, where we need to have a training data, here we define the environment where each actor or agent can perform a set of specific actions. Each action has a reward that depends on the new state and the previous state. In July 2017, Google published a paper and video showing a simulated body trained to navigate through a set of challenging terrains, using reinforcement learning. My project will use a similar approach to train a simulated body to climb a rock wall. This process would be more complex because here every joint would play an important role in the movement. CS297 Results
Proposed Schedule
Key Deliverables:
Innovations and Challenges
References:1. Watts, P. B. (2004). Physiology of difficult rock climbing. European Journal of Applied Physiology, 91(4), 361-372. doi:10.1007/s00421-003-1036-7. 2. Phillips, C., Becker, L., & Bradley, E. (2012). Strange beta: An assistance system for indoor rock climbing route setting. Chaos: An Interdisciplinary Journal of Nonlinear Science, 22(1), 013130. doi:10.1063/1.3693047. 3. Aristidou, A., & Lasenby, J. (2011). FABRIK: A fast, iterative solver for the Inverse Kinematics problem. Graphical Models, 73(5), 243-260. doi:10.1016/j.gmod.2011.05.003. 4. Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013). Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602. 5. Schulman, J., Levine, S., Abbeel, P., Jordan, M., & Moritz, P. (2015, June). Trust region policy optimization. In International Conference on Machine Learning (pp. 1889-1897). 6. Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., ... & Wierstra, D. (2015). Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971. 7. Abbeel, P., & Schulman, J. (2016). Deep reinforcement learning through policy optimization. Tutorial at Neural Information Processing Systems. 8. Heess, N., Sriram, S., Lemmon, J., Merel, J., Wayne, G., Tassa, Y., ... & Silver, D. (2017). Emergence of locomotion behaviours in rich environments. arXiv preprint arXiv:1707.02286. 9. Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347. |