Statistical Convergence of Transformer Encoder-Accelerated Robust Reinforcement Learning

Abstract:Obtaining the optimal action-value function in Markov decision processes is computationally intensive in large state--action spaces. In this study, we present statistically rigorous convergence results for a robust reinforcement learning algorithm warm-started by a transformer-based action-value function prediction, where natural language prompts encode task specifications. Our framework adopts the R-contamination model to characterize uncertainty in the state transition kernel, and employs conformal prediction to certify convergence via trajectory-level nonconformity scores constructed from the contracting Bellman residual. The resulting conformal quantile bounds the gap between the running and optimal action-value functions simultaneously over all iterations, thereby yielding a pre-certified stopping rule that requires little knowledge of the true transition kernel. Numerical case studies on perturbed maze environments of varying size and contamination level confirm that the transformer-based warm start measurably reduces the initial error and accelerates convergence, while the proposed conformal bounds track the true error trajectory more tightly than existing guarantees.

Machine Learning (cs.LG); Systems and Control (eess.SY)

arXiv:2609.23775 [cs.LG]

(or arXiv:2609.23775v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.23775 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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