A Finite-Iteration Theory for Asynchronous Categorical Distributional Temporal-Difference Learning

• URL: http://arxiv.org/abs/2605.06866v1
• Date: Thu, 07 May 2026 19:07:00 GMT
• Title: A Finite-Iteration Theory for Asynchronous Categorical Distributional Temporal-Difference Learning
• Abstract summary: We bridge the gap between finite-iteration theory and the categorical recursions most closely aligned with practical distributional temporal-difference implementations.<n>After suitable embeddings, both algorithms take the form of asynchronous single-state-approximations that contract in a statewise supremum norm.<n>This permits finite-iteration guarantees in discounted problems under both i.i.d. and Markovian state sampling, and in undiscounted fixed-horizon problems under i.i.d. episodic sampling.
• Score: 9.562123195246393
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent non-asymptotic analyses have substantially advanced the theory of distributional policy evaluation, but they largely concern synchronous full-state updates under a generative model, model-based estimators, accelerated variants, or different approximation architectures. Standard categorical temporal-difference learning is typically used in a different regime. It asynchronously performs a single-state update at each iteration and, in online settings, is driven by a Markovian trajectory. This leaves an important gap between existing finite-iteration theory and the categorical recursions most closely aligned with practical distributional temporal-difference implementations. We bridge this gap for two categorical policy-evaluation methods: scalar categorical temporal-difference learning in the Cramér geometry and multivariate signed-categorical temporal-difference learning in the maximum mean discrepancy geometry. After suitable isometric embeddings, both algorithms take the form of asynchronous single-state stochastic-approximation recursions that contract in a statewise supremum norm. This permits finite-iteration guarantees in discounted problems under both i.i.d. and Markovian state sampling, and in undiscounted fixed-horizon problems under i.i.d. episodic sampling.

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