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.
See the Infra-Bayesianism Sequence.