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humans, and then test weather or not the candidate models satisfy the local controlla-bility condition when they are linearized on the recorded trajectories. But even if the controllability condition is satisfied that does not mean that the tested model is a good candidate due to the fact that controls are constrained. More precisely if neural activity is treated as the control variable then it is bounded between 0 and 1 while in cases where the forces produced by the muscles is treated as controls then these control variables have to be positive. Thus, the controllability condition is a necessary but not a sufficient condition for the case of constrained controls. Future research will investigate the application of alternative methods of optimal control such as the Pseudospectral methods. In Pseudospectral methods, the optimal trajectory and control are represented as polynomial functions of time. These methods can handle hard constrains in control and state however they provide open loop optimal policies and not feedback policies. Moreover, they are mostly applicable to deterministic and not stochastic systems. It is really an open question how Pseudospectral methods compare to iterative methods and how they could be applied to bio-mechanical models. 267
Object Description
Title | Iterative path integral stochastic optimal control: theory and applications to motor control |
Author | Theodorou, Evangelos A. |
Author email | etheodor@usc.edu; theo0027@umn.edu |
Degree | Doctor of Philosophy |
Document type | Dissertation |
Degree program | Computer Science |
School | Viterbi School of Engineering |
Date defended/completed | 2011-01-11 |
Date submitted | 2011 |
Restricted until | Unrestricted |
Date published | 2011-04-29 |
Advisor (committee chair) | Schaal, Stefan |
Advisor (committee member) |
Valero-Cuevas, Francisco Sukhatme, Gaurav S. Todorov, Emo Schweighofer, Nicolas |
Abstract | Motivated by the limitations of current optimal control and reinforcement learning methods in terms of their efficiency and scalability, this thesis proposes an iterative stochastic optimal control approach based on the generalized path integral formalism. More precisely, we suggest the use of the framework of stochastic optimal control with path integrals to derive a novel approach to RL with parameterized policies. While solidly grounded in value function estimation and optimal control based on the stochastic Hamilton Jacobi Bellman (HJB) equation, policy improvements can be transformed into an approximation problem of a path integral which has no open algorithmic parameters other than the exploration noise. The resulting algorithm can be conceived of as model-based, semi-model-based, or even model free, depending on how the learning problem is structured. The new algorithm, Policy Improvement with Path Integrals (PI2), demonstrates interesting similarities with previous RL research in the framework of probability matching and provides intuition why the slightly heuristically motivated probability matching approach can actually perform well. Applications to high dimensional robotic systems are presented for a variety of tasks that require optimal planning and gain scheduling.; In addition to the work on generalized path integral stochastic optimal control, in this thesis we extend model based iterative optimal control algorithms to the stochastic setting. More precisely we derive the Differential Dynamic Programming algorithm for stochastic systems with state and control multiplicative noise. Finally, in the last part of this thesis, model based iterative optimal control methods are applied to bio-mechanical models of the index finger with the goal to find the underlying tendon forces applied for the movements of, tapping and flexing. |
Keyword | stochastic optimal control; reinforcement learning,; robotics |
Language | English |
Part of collection | University of Southern California dissertations and theses |
Publisher (of the original version) | University of Southern California |
Place of publication (of the original version) | Los Angeles, California |
Publisher (of the digital version) | University of Southern California. Libraries |
Provenance | Electronically uploaded by the author |
Type | texts |
Legacy record ID | usctheses-m3804 |
Contributing entity | University of Southern California |
Rights | Theodorou, Evangelos A. |
Repository name | Libraries, University of Southern California |
Repository address | Los Angeles, California |
Repository email | cisadmin@lib.usc.edu |
Filename | etd-Theodorou-4581 |
Archival file | uscthesesreloadpub_Volume14/etd-Theodorou-4581.pdf |
Description
Title | Page 281 |
Contributing entity | University of Southern California |
Repository email | cisadmin@lib.usc.edu |
Full text | humans, and then test weather or not the candidate models satisfy the local controlla-bility condition when they are linearized on the recorded trajectories. But even if the controllability condition is satisfied that does not mean that the tested model is a good candidate due to the fact that controls are constrained. More precisely if neural activity is treated as the control variable then it is bounded between 0 and 1 while in cases where the forces produced by the muscles is treated as controls then these control variables have to be positive. Thus, the controllability condition is a necessary but not a sufficient condition for the case of constrained controls. Future research will investigate the application of alternative methods of optimal control such as the Pseudospectral methods. In Pseudospectral methods, the optimal trajectory and control are represented as polynomial functions of time. These methods can handle hard constrains in control and state however they provide open loop optimal policies and not feedback policies. Moreover, they are mostly applicable to deterministic and not stochastic systems. It is really an open question how Pseudospectral methods compare to iterative methods and how they could be applied to bio-mechanical models. 267 |