| Autor: |
Williams, Kyle R., Schlossman, Rachel, Whitten, Daniel, Ingram, Joe, Musuvathy, Srideep, Patel, Anirudh, Pagan, James, Williams, Kyle A., Green, Sam, Mazumdar, Anirban, Parish, Julie |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
IEEE Transactions on Aerospace and Electronic Systems, 59 (2023) 2513-2529 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/TAES.2022.3218496 |
| Popis: |
This paper presents a technique for trajectory planning based on continuously parameterized high-level actions (motion primitives) of variable duration. This technique leverages deep reinforcement learning (Deep RL) to formulate a policy which is suitable for real-time implementation. There is no separation of motion primitive generation and trajectory planning: each individual short-horizon motion is formed during the Deep RL training to achieve the full-horizon objective. Effectiveness of the technique is demonstrated numerically on a well-studied trajectory generation problem and a planning problem on a known obstacle-rich map. This paper also develops a new loss function term for policy-gradient-based Deep RL, which is analogous to an anti-windup mechanism in feedback control. We demonstrate the inclusion of this new term in the underlying optimization increases the average policy return in our numerical example. |
| Databáze: |
arXiv |
| Externí odkaz: |
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