Recursive Compositional Reinforcement Learning for Continuous Control Sürekli Kontrol Uygulamalari için Özyinelemeli Bileşimsel Pekiştirmeli Öǧrenme

Date

2022-01-01

Author

Tanik, Guven Orkun
Ertekin Bolelli, Şeyda

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Compositional and temporal abstraction is the key to improving learning and planning in reinforcement learning. Modern real-world control problems call for continuous control domains and robust, sample efficient and explainable control frameworks. We are presenting a framework for recursively composing control skills to solve compositional and progressively complex tasks. The framework promotes reuse of skills, and as a result quickly adaptable to new tasks. The decision-tree can be observed, providing insight to the agents' behavior. Furthermore, the skills can be transferred, modified or trained independently, which can simplify reward shaping and increase training speeds considerably.

Subject Keywords

actor-critic, beta distribution, hierarchical learning, reinforcement learning

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85138721127&origin=inward
https://hdl.handle.net/11511/101422

DOI

https://doi.org/10.1109/siu55565.2022.9864891

Conference Name

30th Signal Processing and Communications Applications Conference, SIU 2022

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

G. O. Tanik and Ş. Ertekin Bolelli, “Recursive Compositional Reinforcement Learning for Continuous Control Sürekli Kontrol Uygulamalari için Özyinelemeli Bileşimsel Pekiştirmeli Öǧrenme,” presented at the 30th Signal Processing and Communications Applications Conference, SIU 2022, Safranbolu, Türkiye, 2022, Accessed: 00, 2023. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85138721127&origin=inward.