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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
Metadata
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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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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.