Infra-Bayesianism is a new approach to epistemology /​ decision theory /​ reinforcement learning theory, which builds on “imprecise probability” to solve the problem of prior misspecification /​ grain-of-truth /​ nonrealizability which plagues Bayesianism and Bayesian reinforcement learning.

Infra-Bayesianism also naturally leads to an implementation of UDT, and (more speculatively at this stage) has applications to multi-agent theory, embedded agency and reflection. This sequence lays down the foundations of the approach.