Jose Blanchet, Jiayi Cheng, Hao Liu, Yang Liu
The paper introduces a distributionally robust Bayesian control framework for diffusion processes, addressing model misspecification by using adversarial priors within a divergence neighborhood, and provides an efficient algorithm for optimal strategy computation.
This research addresses a common problem in control systems where the model used to predict outcomes is often not perfectly accurate. The authors propose a new method that accounts for these inaccuracies by considering a range of possible models, rather than relying on just one. This approach uses concepts from game theory, where an adversary selects alternative models within a certain range, to ensure the controller's strategy remains effective even if the original model is slightly wrong. The paper demonstrates that this method can be efficiently implemented using modern computational techniques, such as neural networks.
