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Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces
Keio Univ, KTH, RIKEN, Tokyo, Japan..
Keio Univ, RIKEN, Tokyo, Japan..
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
Univ Tokyo, RIKEN, Tokyo, Japan..
2018 (English)In: Advances in Neural Information Processing Systems 31 (NIPS 2018) / [ed] Bengio, S Wallach, H Larochelle, H Grauman, K CesaBianchi, N Garnett, R, Neural Information Processing Systems (NIPS) , 2018, Vol. 31Conference paper, Published paper (Refereed)
Abstract [en]

Motivated by the success of reinforcement learning (RL) for discrete-time tasks such as AlphaGo and Atari games, there has been a recent surge of interest in using RL for continuous-time control of physical systems (cf. many challenging tasks in OpenAI Gym and DeepMind Control Suite). Since discretization of time is susceptible to error, it is methodologically more desirable to handle the system dynamics directly in continuous time. However, very few techniques exist for continuous-time RL and they lack flexibility in value function approximation. In this paper, we propose a novel framework for model-based continuous-time value function approximation in reproducing kernel Hilbert spaces. The resulting framework is so flexible that it can accommodate any kind of kernel-based approach, such as Gaussian processes and kernel adaptive filters, and it allows us to handle uncertainties and nonstationarity without prior knowledge about the environment or what basis functions to employ. We demonstrate the validity of the presented framework through experiments.

Place, publisher, year, edition, pages
Neural Information Processing Systems (NIPS) , 2018. Vol. 31
Series
Advances in Neural Information Processing Systems, ISSN 1049-5258 ; 31
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-249920ISI: 000461823302080OAI: oai:DiVA.org:kth-249920DiVA, id: diva2:1307197
Conference
32nd Conference on Neural Information Processing Systems (NIPS), DEC 02-08, 2018, Montreal, CANADA
Note

QC 20190426

Available from: 2019-04-26 Created: 2019-04-26 Last updated: 2019-04-26Bibliographically approved

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Johansson, Mikael

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