The Value Iteration Algorithm is Not Strongly Polynomial for Discounted Dynamic Programming
Not on the map. Why?
Abstract (arXiv)
This note provides a simple example demonstrating that, if exact computations are allowed, the number of iterations required for the value iteration algorithm to find an optimal policy for discounted dynamic programming problems may grow arbitrarily quickly with the size of the problem. In particular, the number of iterations can be exponential in the number of actions. Thus, unlike policy iterations, the value iteration algorithm is not strongly polynomial for discounted dynamic programming.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Dec 2013.
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Citing papers per year: 2024: 1; 2025: 3.
4 papers.
- 2025On the Hardness of Reinforcement Learning with Transition Look-Aheadcited by 4
- 2024MDP Geometry, Normalization and Reward Balancing Solverscited by 2
- 2025Geometric Re-Analysis of Classical MDP Solving Algorithmscited by 1
- 2025Revisiting Value Iteration: Unified Analysis of Discounted and Average-Reward Casescited by 1
- 2025Revisiting Value Iteration: Unified Analysis of Discounted and Average-Reward Cases27 Oct 2025
- 2025On the Hardness of Reinforcement Learning with Transition Look-Ahead22 Oct 2025
- 2025Geometric Re-Analysis of Classical MDP Solving Algorithms6 Mar 2025
- 2024MDP Geometry, Normalization and Reward Balancing Solvers9 Jul 2024