mlsanityData to 7 Oct 2026

On the convergence of optimistic policy iteration for stochastic shortest path problem

arXiv 1808.08763 ↗First posted 27 Aug 2018cs.LG stat.MLcited by 9

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Abstract (arXiv)

In this paper, we prove some convergence results of a special case of optimistic policy iteration algorithm for stochastic shortest path problem. We consider both Monte Carlo and TD(λ) methods for the policy evaluation step under the condition that the termination state will eventually be reached almost surely.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Aug 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 2; 2025: 1; 2026: 3.

9 papers.

  1. 2023On The Convergence Of Policy Iteration-Based Reinforcement Learning With Monte Carlo Policy Evaluationcited by 8
  2. 2021Optimistic Policy Iteration for MDPs with Acyclic Transient State Structurecited by 5
  3. 2022On the Convergence of Monte Carlo UCB for Random-Length Episodic MDPscited by 2
  4. 2026Convergence of Monte Carlo Optimistic Policy Iteration: Beyond Uniform State-Action Updatescited by 2
  5. 2024Finite-Sample Analysis of the Monte Carlo Exploring Starts Algorithm for Reinforcement Learningcited by 1
  6. 2025Stochastic Path Planning in Correlated Obstacle Fieldscited by 1
  7. 2024Simulation-Based Optimistic Policy Iteration For Multi-Agent MDPs with Kullback-Leibler Control Costcited by 0
  8. 2026Exploring Starts Are Not Enough: Counterexamples and a Fix for Monte Carlo Exploring Startscited by 0
  9. 2026Scalar-Stepsize Nonuniform Monte Carlo Optimistic Policy Iteration: A Certified Counterexamplecited by 0