A Bayesian composite risk approach for stochastic optimal control and Markov decision processes

Abstract Inspired by Shapiro et al. [74], we consider a stochastic optimal control (SOC) and Markov decision process (MDP) under simultaneous epistemic and aleatoric uncertainties using Bayesian composite risk (BCR) measures. The proposed BCR-SOC/MDP model evaluates the risk of stagewise cost via a two-layer framework: the inner risk measure tackles aleatoric uncertainty conditional on a latent environment parameter, while the outer risk measure deals with the epistemic uncertainty of the inner risk under the Bayesian posterior. The resulting time-varying risk evaluation induced by Bayesian updating enables an information-adaptive risk-sensitive decision framework. Unlike [74], our policies are allowed to depend explicitly on the posterior belief, reflecting that accumulated information about epistemic uncertainty can influence the assessment of future aleatoric uncertainty and, consequently, the decision maker’s actions [79]. The new modeling paradigm subsumes several classical SOC/MDP formulations, including risk-averse and distributionally robust SOC/MDPs as well as partially observed and Bayes-adaptive MDPs, and generates so-called preference robust SOC/MDP models. Moreover, we derive conditions under which the BCR-SOC/MDP model is well-defined, show that finite-horizon BCR-SOC/MDP models can be solved via dynamic programming, and extend the analysis to the infinite-horizon case. Under standard conditions, we establish asymptotic convergence of the optimal values and optimal policies as data accumulate, and provide quantitative error bounds for several representative classes of risk measures. To enhance computational tractability, we develop a hyper-parameter discretization approach for the posterior belief space. Finally, we carry out numerical tests on a spread betting problem and an inventory control problem, demonstrating the effectiveness of the proposed model and numerical schemes.