mlsanityData to 14 Oct 2026

Statistical Convergence of Transformer Encoder-Accelerated Robust Reinforcement Learning

arXiv 2609.23775 ↗First posted 20 Sep 2026cs.LG cs.SY eess.SYcited by 0

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 15 of its 26 references that are in the map. First posted on arXiv 20 Sep 2026.

Builds on

15 of 26 references.

References · 15

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  4. 2017Robust Adversarial Reinforcement Learningcited by 451
  5. 2020Learning Invariant Representations for Reinforcement Learning without Reconstructioncited by 436
  6. 2019Stabilizing Transformers for Reinforcement Learningcited by 270
  7. 2019The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectivescited by 139
  8. 2021Online Robust Reinforcement Learning with Model Uncertaintycited by 96
  9. 2020Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reductioncited by 95
  10. 2022Policy Gradient Method For Robust Reinforcement Learningcited by 69
  11. 2021CoBERL: Contrastive BERT for Reinforcement Learningcited by 21
  12. 2023Conformal Predictive Safety Filter for RL Controllers in Dynamic Environmentscited by 14
3 more
  1. 2025ORVIT: Near-Optimal Online Distributionally Robust Reinforcement Learningcited by 6
  2. 2023CAMMARL: Conformal Action Modeling in Multi Agent Reinforcement Learningcited by 3
  3. 2025Statistical Guarantees in Data-Driven Nonlinear Control: Conformal Robustness for Stability and Safetycited by 3

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  1. 2025Context Representation via Action-Free Transformer encoder-decoder for Meta Reinforcement Learning
  2. 2021Decision Transformer: Reinforcement Learning via Sequence Modeling
  3. 2026Uncertainty-Aware LLM-Guided Policy Shaping for Sparse-Reward Reinforcement Learning
  4. 2024Q-value Regularized Transformer for Offline Reinforcement Learning
  5. 2025Viability of Future Actions: Robust Safety in Reinforcement Learning via Entropy Regularization
  6. 2025Robust Adversarial Reinforcement Learning in Stochastic Games via Sequence Modeling
  7. 2026Q-learning Penalized Transformer for Safe Offline Reinforcement Learning
  8. 2026From Weak Data to Strong Policy: Q-Targets Enable Provable In-Context Reinforcement Learning