Dorobantu, Victor D. and Azizzadenesheli, Kamyar and Yue, Yisong (2022) Compactly Restrictable Metric Policy Optimization Problems. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20220714-212414777
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Abstract
We study policy optimization problems for deterministic Markov decision processes (MDPs) with metric state and action spaces, which we refer to as Metric Policy Optimization Problems (MPOPs). Our goal is to establish theoretical results on the well-posedness of MPOPs that can characterize practically relevant continuous control systems. To do so, we define a special class of MPOPs called Compactly Restrictable MPOPs (CR-MPOPs), which are flexible enough to capture the complex behavior of robotic systems but specific enough to admit solutions using dynamic programming methods such as value iteration. We show how to arrive at CR-MPOPs using forward-invariance. We further show that our theoretical results on CR-MPOPs can be used to characterize feedback linearizable control affine systems.
Item Type: | Report or Paper (Discussion Paper) | ||||||||
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Additional Information: | Submitted May 15th, 2021. Resubmitted July 6th, 2022. This work was supported in part by DARPA and Beyond Limits. Victor D. Dorobantu was also supported in part by a Kortschak Fellowship. | ||||||||
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Subject Keywords: | Continuous Markov Decision Processes, Reinforcement Learning, Optimal Control, Value Iteration, Selection Theorems, Sampled-Data, Physical Systems | ||||||||
Record Number: | CaltechAUTHORS:20220714-212414777 | ||||||||
Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20220714-212414777 | ||||||||
Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||
ID Code: | 115568 | ||||||||
Collection: | CaltechAUTHORS | ||||||||
Deposited By: | George Porter | ||||||||
Deposited On: | 15 Jul 2022 22:38 | ||||||||
Last Modified: | 15 Jul 2022 22:38 |
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