Transformer predictions speed up robust reinforcement learning convergence
Statistical Convergence of Transformer Encoder-Accelerated Robust Reinforcement Learning
Machine Learning
Summary
Finding the best actions to take in complex decision tasks is very slow with big state and action spaces. The authors show that starting reinforcement learning with action-value predictions from a transformer model, which uses natural language to understand tasks, helps the learning process go faster and more accurately. They also use math techniques to confidently measure how close the learning is to the best solution at all times. Their tests on maze-like problems show this approach reduces errors early on and tracks learning progress more precisely than older methods.
What this means in practice
- •For robotic control engineers: Improve control algorithms in robots operating under uncertain conditions by using transformer-based guesses to accelerate safe decision learning.
- •For game ai developers: Enhance training of AI agents in complex games by providing faster convergence guarantees that adapt to environment uncertainty.
Tested on simulated data.
Authors
Suman Banerjee, Hiroyasu Tsukamoto
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.