Self-Normalized Inference for Constant-Stepsize Temporal-Difference Learning under Markovian Sampling
Constant-stepsize temporal-difference (TD) learning is attractive for policy evaluation, but inference from a single Markov trajectory must account for serial dependence and a stepsize-dependent stationary target. For fixed-stepsize linear TD, we establish a functional central limit theorem whose covariance retains the multiplicative component induced by the random TD matrix and the stationary iterate error. We then derive a joint functional limit for parallel Richardson--Romberg (RR) recursions driven by the same trajectory. A Brownian-bridge self-normalizer yields asymptotically pivotal confidence regions for prespecified state-value contrasts without estimating the long-run covariance ...
| Authors | Min Zeng, Yichen Zhang, Xiaofeng Shao |
|---|---|
| Primary category | stat.ML |
| Published | 11 August 2026 |
| Last updated at source | 11 August 2026 |
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