mlsanityData to 14 Oct 2026

Beyond the Bellman Fixed Point: Geometry and Fast Policy Identification in Value Iteration

arXiv 2604.17457 ↗First posted 19 Apr 2026math.OC cs.AI cs.SYcited by 1

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Abstract (arXiv)

Q-value iteration (Q-VI) is usually analyzed through the (γ)-contraction of the Bellman operator. This argument proves convergence to (Q^*), but it gives only a coarse account of when the induced greedy policy becomes optimal. We study discounted Q-VI as a switching system and focus on the practically optimal solution set (POSS), the set of (Q)-functions whose tie-broken greedy policies are optimal. The main result shows that Q-VI reaches the optimal action class in finite time by entering an invariant tube around (mathcal X1=Q^*+operatornamespan(mathbf 1)), which is contained in the POSS. For every (varepsilon>0), the distance to (mathcal X1) satisfies an exponential bound with rate ((barρ+varepsilon)k), where (barρ) is the joint spectral radius of the projected switching family restricted to directions transverse to (mathcal X1). When (barρ<γ), this transverse convergence is faster than the classical contraction rate. The analysis separates fast policy identification from the subsequent convergence to (Q^*), which may still be governed by the all-ones mode. We also give spectral and graph-theoretic conditions under which the strict inequality (barρ<γ) holds or fails.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 13 of its 40 references that are in the map. First posted on arXiv 19 Apr 2026.

Builds on

13 of 40 references.

References · 13

  1. 2019The Value Function Polytope in Reinforcement Learningcited by 29
  2. 2019A First-Order Approach To Accelerated Value Iterationcited by 17
  3. 2021A Discrete-Time Switching System Analysis of Q-learningcited by 16
  4. 2025A Non-Asymptotic Theory of Seminorm Lyapunov Stability: From Deterministic to Stochastic Iterative Algorithmscited by 10
  5. 2022Final Iteration Convergence Bound of Q-Learning: Switching System Approachcited by 9
  6. 2022Dynamic Programming Through the Lens of Semismooth Newton-Type Methods (Extended Version)cited by 7
  7. 2022Finite-Time Analysis of Asynchronous Q-learning under Diminishing Step-Size from Control-Theoretic Viewcited by 4
  8. 2022Convex Programs and Lyapunov Functions for Reinforcement Learning: A Unified Perspective on the Analysis of Value-Based Methodscited by 3
  9. 2022Geometric Policy Iteration for Markov Decision Processescited by 3
  10. 2023On Some Geometric Behavior of Value Iteration on the Orthant: Switching System Perspectivecited by 1
  11. 2024On Value Iteration Convergence in Connected MDPscited by 1
  12. 2025Analysis of Value Iteration Through Absolute Probability Sequencescited by 1
1 more
  1. 2025Geometric Re-Analysis of Classical MDP Solving Algorithmscited by 1

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Citing papers per year: 2026: 1.

1 paper.