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Efficient Reinforcement Learning in Deterministic Systems with Value Function Generalization

Author

Listed:
  • Zheng Wen

    (Adobe Research, San Jose, California 95110)

  • Benjamin Van Roy

    (Stanford University, Stanford, California 94305)

Abstract

We consider the problem of reinforcement learning over episodes of a finite-horizon deterministic system and as a solution propose optimistic constraint propagation ( OCP ), an algorithm designed to synthesize efficient exploration and value function generalization. We establish that when the true value function lies within a given hypothesis class, OCP selects optimal actions over all but at most D episodes, where D is the eluder dimension of the given hypothesis class. We establish further efficiency and asymptotic performance guarantees that apply even if the true value function does not lie in the given hypothesis class, for the special case where the hypothesis class is the span of prespecified indicator functions over disjoint sets. We also discuss the computational complexity of OCP and present computational results involving two illustrative examples.

Suggested Citation

  • Zheng Wen & Benjamin Van Roy, 2017. "Efficient Reinforcement Learning in Deterministic Systems with Value Function Generalization," Mathematics of Operations Research, INFORMS, vol. 42(3), pages 762-782, August.
    • Handle: RePEc:inm:ormoor:v:42:y:2017:i:3:p:762-782
    DOI: 10.1287/moor.2016.0826
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    References listed on IDEAS

    as
    1. Benjamin Van Roy, 2006. "Performance Loss Bounds for Approximate Value Iteration with State Aggregation," Mathematics of Operations Research, INFORMS, vol. 31(2), pages 234-244, May.
    2. Daniel Russo & Benjamin Van Roy, 2014. "Learning to Optimize via Posterior Sampling," Mathematics of Operations Research, INFORMS, vol. 39(4), pages 1221-1243, November.
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