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Statistical Convergence of Transformer Encoder-Accelerated Robust Reinforcement Learning

arXiv · AI, language, vision and robotics · article · Sep 20, 2026 · UTC

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 t

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