🧪 Test?View on arXiv
Regularized Emphatic Temporal-Difference Learning: Stability under Constant Stepsizes
Not provided in the abstract
temporal-difference learningreinforcement learningstability analysis
2609.19170
Builder Relevance
2h ago70%
Abstract
This paper introduces regularized emphatic temporal-difference learning (RETD) to achieve stability under constant stepsizes in off-policy TD learning.
Reality Card
Core Claim
RETD achieves almost-sure convergence with harmonic diminishing stepsizes and demonstrates a conditional constant-stepsize moment-contraction result.
Method / Result
RETD has certified negative exponents on a two-state construction and one Baird point.
Limitations
The positive Baird ETD sign remains numerical, which may affect reproducibility.
Paper to code
Verified implementation resources so builders can test the paper’s claims instead of stopping at the abstract.
No verified implementation link has been attached yet. AIBuzzHub will keep this panel separate from unverified search results.