PAC model-free reinforcement learning

ABSTRACT

For a Markov Decision Process with finite state (size S) and action spaces (size A per state), we propose a new algorithm---Delayed Q-Learning. We prove it is PAC, achieving near optimal performance except for Õ(SA) timesteps using O(SA) space, improving on the Õ(S² A) bounds of best previous algorithms. This result proves efficient reinforcement learning is possible without learning a model of the MDP from experience. Learning takes place from a single continuous thread of experience---no resets nor parallel sampling is used. Beyond its smaller storage and experience requirements, Delayed Q-learning's per-experience computation cost is much less than that of previous PAC algorithms.

REFERENCES

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.8 Problem Solving, Control Methods, and Search
          Subjects: Backtracking

Additional Classification:
  G. Mathematics of Computing
  G.3 PROBABILITY AND STATISTICS
      Subjects: Markov processes

  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.2 Design Methodology

General Terms:
Algorithms