Hierarchical reinforcement learning (HRL) can provide a principled solution to the RL challenge of scalability for complex tasks. By incorporating a graphical model (GM) and the rich family of related methods, there is also hope to address issues such as transferability, generalisation and exploration. Here we propose a flexible GM-based HRL framework which leverages efficient inference procedures to enhance generalisation and transfer power. In our proposed transferable and information-based graphical model framework ‘TibGM’, we show the equivalence between our mutual information-based objective in the GM, and an RL consolidated objective consisting of a standard reward maximisation target and a generalisation/transfer objective. In settings where there is a sparse or deceptive reward signal, our TibGM framework is flexible enough to incorporate exploration bonuses depicting intrinsic rewards. We empirically verify improved performance and exploration power.