Abstract:
This seminar introduces Partially Observable Markov Decision Processes (POMDPs), emphasizing their relationship with decision problems and decidability. We then explore a novel theoretical class of POMDPs based on a weak ergodicity property. The seminar concludes by identifying necessary conditions for POMDPs to exhibit this characteristic, bridging the gap between this theoretical result and its potential practical implementation.