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ATAL
2015
Springer

Predictive State Representations with State Space Partitioning

3 years 4 days ago
Predictive State Representations with State Space Partitioning
Predictive state representations (PSRs) are powerful methods of modeling dynamical systems by representing state through observational data. Most of the current PSR techniques focus on learning a complete PSR model from the entire state space. Consequently, the techniques are often not scalable due to the dimensional curse, which limits applications of PSR. In this paper, we propose a new PSR learning technique. Instead of directly learning a complete PSR at one time, we learn a set of local models each of which is constructed on a sub-state space and then combine the learnt models. We employ the landmark technique to partition the entire state space. We further show the theoretical guarantees on the learning performance of the proposed technique and present empirical results on multiple domains. Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Learning General Terms Algorithms, Theory Keywords Predictive State Representations; state space partitioning; landmark
Yunlong Liu, Yun Tang, Yifeng Zeng
Added 16 Apr 2016
Updated 16 Apr 2016
Type Journal
Year 2015
Where ATAL
Authors Yunlong Liu, Yun Tang, Yifeng Zeng
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