Bayesian learning for the stochastic shortest path problem
Sequential decision-making problems are often modelled as a Markov decision process (MDP). We focus on the stochastic shortest path (SSP) problem, which is an infinite-horizon undiscounted MDP with absorbing terminal states. We develop a Bayesian framework to learn the optimal decision strategy through interactions with the decision-making task. Specifically, we learn the optimal action-value function $Q^*$, but unlike many existing Bayesian approaches, we do not rely on unrealistic modelling assumptions and ad-hoc approximations. Our approach is to directly construct the posterior beliefs for $Q^*$ through Bellman's optimality equations. For deterministic rewards, we characterise the pos...
| Authors | Chon Wai Ho, Sumeetpal S. Singh, Jiaqi Guo |
|---|---|
| Primary category | stat.ML |
| Published | 3 June 2026 |
| Last updated at source | 3 June 2026 |
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