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Optimal Direct Policy Search

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 6830)

Abstract

Hutter’s optimal universal but incomputable AIXI agent models the environment as an initially unknown probability distribution-computing program. Once the latter is found through (incomputable) exhaustive search, classical planning yields an optimal policy. Here we reverse the roles of agent and environment by assuming a computable optimal policy realizable as a program mapping histories to actions. This assumption is powerful for two reasons: (1) The environment need not be probabilistically computable, which allows for dealing with truly stochastic environments, (2) All candidate policies are computable. In stochastic settings, our novel method Optimal Direct Policy Search (ODPS) identifies the best policy by direct universal search in the space of all possible computable policies. Unlike AIXI, it is computable, model-free, and does not require planning. We show that ODPS is optimal in the sense that its reward converges to the reward of the optimal policy in a very broad class of partially observable stochastic environments.

Keywords

  • Optimal Policy
  • Reinforcement Learning
  • Turing Machine
  • Markov Decision Process
  • Direct Policy

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Hutter, M.: Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability. Springer, Berlin (2004)

    Google Scholar 

  2. Levin, L.: Universal sequential search problems. Problems of Information Transmission 9(3), 265–266 (1973)

    Google Scholar 

  3. Schaul, T., Schmidhuber, J.: Towards Practical Universal Search. In: Proceedings of the Third Conference on Artificial General Intelligence, Lugano (2010)

    Google Scholar 

  4. Schmidhuber, J.: Sequential decision making based on direct search (Lecture Notes on AI 1828). In: Sun, R., Giles, C.L. (eds.) IJCAI-WS 1999. LNCS (LNAI), vol. 1828, p. 213. Springer, Heidelberg (2001)

    CrossRef  Google Scholar 

  5. Schmidhuber, J.: Optimal Ordered Problem Solver. Machine Learning 54, 211–254 (2004)

    MATH  CrossRef  Google Scholar 

  6. Schmidhuber, J.: Gödel machines: Fully Self-Referential Optimal Universal Self-Improvers. In: Goertzel, B., Pennachin, C. (eds.) Artificial General Intelligence, pp. 119–226 (2006)

    Google Scholar 

  7. Schmidhuber, J.: Ultimate Cognition à la Gödel. Cognitive Computation 1(2), 177–193 (2009)

    CrossRef  Google Scholar 

  8. Schultz, W., Dayan, P., Montague, P.R.: A neural substrate of prediction and reward. Science 275(5306), 1593 (1997)

    CrossRef  Google Scholar 

  9. Veness, J., Ng, K.S., Hutter, M., Silver, D.: A Monte Carlo AIXI Approximation. Technical Report 0909.0801, arXiv (2009)

    Google Scholar 

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Glasmachers, T., Schmidhuber, J. (2011). Optimal Direct Policy Search. In: Schmidhuber, J., Thórisson, K.R., Looks, M. (eds) Artificial General Intelligence. AGI 2011. Lecture Notes in Computer Science(), vol 6830. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22887-2_6

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  • DOI: https://doi.org/10.1007/978-3-642-22887-2_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22886-5

  • Online ISBN: 978-3-642-22887-2

  • eBook Packages: Computer ScienceComputer Science (R0)