Abstraction in reinforcement learning in partially observable environments

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2014

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Çilden, Erkin

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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 thesis, 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., Machine learning., Sequential analysis., Markov processes.

URI

http://etd.lib.metu.edu.tr/upload/12616815/index.pdf
https://hdl.handle.net/11511/23288

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

E. Çilden, “Abstraction in reinforcement learning in partially observable environments,” Ph.D. - Doctoral Program, Middle East Technical University, 2014.