Deterministic Framework based Structured Learning for Quadrotors

Rupam Singh*, Jan Steinbrener, Stephan Weiss

*Kontaktforfatter

Publikation: Kapitel i bog/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

Abstract

The design of a continuous learning controller for quadrotors often entails some specific implementations that require significant system knowledge and are prone to experience catastrophic forgetting. To address these challenges, a deterministic approach is trained using a quadrotor on a relatively small amount of automatically generated data. The Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is utilized to develop the policy for learning the maneuvers of a quadrotor and controlling it alongside the low-level controller. The algorithm outlined demonstrates proficiency in handling large state spaces and actions that are continuous. It integrates clipped double Q-learning, target policy smoothing, and delayed policy updates, all of which contribute to its effectiveness in training. The proposed control technique's efficacy is evaluated through numerical simulations conducted on a quadrotor in both standard and windy conditions. The results identified that learning with TD3 reduced the overestimation bias, improved the convergence accuracy, and achieved efficient maneuver with less tracking error by using the dense reward structure.

OriginalsprogEngelsk
Titel2023 27th International Conference on Methods and Models in Automation and Robotics (MMAR)
ForlagIEEE
Publikationsdato2023
Sider99-104
ISBN (Elektronisk)9798350311075
DOI
StatusUdgivet - 2023
Begivenhed27th International Conference on Methods and Models in Automation and Robotics, MMAR 2023 - Virtual, Online, Polen
Varighed: 22. aug. 202325. aug. 2023

Konference

Konference27th International Conference on Methods and Models in Automation and Robotics, MMAR 2023
Land/OmrådePolen
ByVirtual, Online
Periode22/08/202325/08/2023
NavnInternational Conference on Methods and Models in Automation and Robotics
ISSN2835-2815

Bibliografisk note

Funding Information:
This work was supported by the Austrian Science Fund (FWF): TAI 183.

Publisher Copyright:
© 2023 IEEE.

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