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1 results

  • Chapter Eleven - Simulation with Function Approximation

  • Martin L. Puterman, University of British Columbia, Vancouver, Timothy C. Y. Chan, University of Toronto
  • Book:
    Markov Decision Processes and Reinforcement Learning
    Print publication:
    10 December 2026, pp 653-732

  • Summary

    This chapter combines the methods from the previous two chapters, namely, function approximation and simulation. It introduces policy-centric methods that are based on approximating the action-choice probability distribution of a randomized policy. Value function approximation using Monte Carlo and temporal differencing methods is described. Optimization algorithms classified as value-based algorithms (Q-learning and Q-policy iteration) and policy-based algorithms (policy gradient and actor-critic) are presented. The chapter concludes with brief remarks on additional topics, including simultaneous learning and control, experience replay, deep learning, importance sampling, Monte Carlo tree search, and partially observable models.