Kısmi gözlemlenebilir takviye öğrenme için dolaysız soyutlama

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TVRRMU5qYzM.pdf

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2015

Author

Çilden, Erkin
Polat, Faruk
Şahin, Coşkun

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Reinforcement learning defines a prominent family of unsupervised machine learning methods in autonomous agents perspective. Markov decision process model provides a solid formal basis for reinforcement learning algorithms. Temporal abstraction mechanisms can be built on reinforcement learning and significant performance gain can be achieved. If the full observability assumption of Markov decision process model is relaxed, the resulting model is partially observable Markov decision process, which constitutes a more realistic but difficult problem setting. Reinforcement learning research for partial observability focuses on techniques to reduce negative impact of perceptual aliasing and huge state-space. In the broadest sense, these studies can be divided into two categories. Model based approaches assume that the state transition model is available to the agent. In the model free approaches, states are completely hidden from the agent. In this project, we propose methods to generalize a known sequence based automatic temporal abstraction technique –namely, extended sequence tree method– to partial observability. We attack the problem in both model based and model free approaches, showing that our methods accelerate well known representatives of each perspective. Effectiveness of our methods are demonstrated by conducting experimentation on widely accepted benchmark problems.

Subject Keywords

Reinforcement Learning, Partially Observable Markov Decision Process, Temporal Abstraction, Extended Sequence Tree

URI

https://app.trdizin.gov.tr/publication/project/detail/TVRRMU5qYzM
https://hdl.handle.net/11511/49848

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Department of Computer Engineering, Project and Design

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Toward Generalization of Automated Temporal Abstraction to Partially Observable Reinforcement Learning Cilden, Erkin; Polat, Faruk (2015-08-01) Temporal abstraction for reinforcement learning (RL) aims to decrease learning time by making use of repeated sub-policy patterns in the learning task. Automatic extraction of abstractions during RL process is difficult but has many challenges such as dealing with the curse of dimensionality. Various studies have explored the subject under the assumption that the problem domain is fully observable by the learning agent. Learning abstractions for partially observable RL is a relatively less explored area. In...

Citation Formats

E. Çilden, F. Polat, and C. Şahin, “Kısmi gözlemlenebilir takviye öğrenme için dolaysız soyutlama,” 2015. Accessed: 00, 2020. [Online]. Available: https://app.trdizin.gov.tr/publication/project/detail/TVRRMU5qYzM.