2 Search Results for "Ryabko, Daniil"

Document

Sequence prediction for non-stationary processes

Authors: Daniil Ryabko and Marcus Hutter

Published in: Dagstuhl Seminar Proceedings, Volume 6201, Combinatorial and Algorithmic Foundations of Pattern and Association Discovery (2006)

Abstract

We address the problem of sequence prediction for nonstationary stochastic processes. In particular, given two measures on the set of one-way infinite sequences over a finite alphabet, consider the question whether one of the measures predicts the other. We find some conditions on local absolute continuity under which prediction is possible.

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Daniil Ryabko and Marcus Hutter. Sequence prediction for non-stationary processes. In Combinatorial and Algorithmic Foundations of Pattern and Association Discovery. Dagstuhl Seminar Proceedings, Volume 6201, pp. 1-12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{ryabko_et_al:DagSemProc.06201.6,
  author =	{Ryabko, Daniil and Hutter, Marcus},
  title =	{{Sequence prediction for non-stationary processes}},
  booktitle =	{Combinatorial and Algorithmic Foundations of Pattern and Association Discovery},
  pages =	{1--12},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{6201},
  editor =	{Rudolf Ahlswede and Alberto Apostolico and Vladimir I. Levenshtein},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.06201.6},
  URN =		{urn:nbn:de:0030-drops-7900},
  doi =		{10.4230/DagSemProc.06201.6},
  annote =	{Keywords: Sequence prediction, probability forecasting, local absolute continuity}
}

Document

DOI: 10.4230/DagSemProc.06051.8

Learning in Reactive Environments with Arbitrary Dependence

Authors: Daniil Ryabko and Marcus Hutter

Published in: Dagstuhl Seminar Proceedings, Volume 6051, Kolmogorov Complexity and Applications (2006)

Abstract

In reinforcement learning the task for an agent is to attain the best possible asymptotic reward where the true generating environment is unknown but belongs to a known countable family of environments. This task generalises the sequence prediction problem, in which the environment does not react to the behaviour of the agent. Solomonoff induction solves the sequence prediction problem for any countable class of measures; however, it is easy to see that such result is impossible for reinforcement learning - not any countable class of environments can be learnt. We find some sufficient conditions on the class of environments under which an agent exists which attains the best asymptotic reward for any environment in the class. We analyze how tight these conditions are and how they relate to different probabilistic assumptions known in reinforcement learning and related fields, such as Markov Decision Processes and mixing conditions.

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Daniil Ryabko and Marcus Hutter. Learning in Reactive Environments with Arbitrary Dependence. In Kolmogorov Complexity and Applications. Dagstuhl Seminar Proceedings, Volume 6051, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{ryabko_et_al:DagSemProc.06051.8,
  author =	{Ryabko, Daniil and Hutter, Marcus},
  title =	{{Learning in  Reactive Environments with Arbitrary Dependence}},
  booktitle =	{Kolmogorov Complexity and Applications},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{6051},
  editor =	{Marcus Hutter and Wolfgang Merkle and Paul M.B. Vitanyi},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.06051.8},
  URN =		{urn:nbn:de:0030-drops-6372},
  doi =		{10.4230/DagSemProc.06051.8},
  annote =	{Keywords: Reinforcement learning, asymptotic average value, self-optimizing policies, (non) Markov decision processes}
}

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2 Search Results for "Ryabko, Daniil"

Sequence prediction for non-stationary processes

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Learning in Reactive Environments with Arbitrary Dependence

Abstract

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