  We propose a novel Reinforcement Learning model for discrete environments,
which is inherently interpretable and supports the discovery of deep subgoal
hierarchies. In the model, an agent learns information about environment in the
form of probabilistic rules, while policies for (sub)goals are learned as
combinations thereof. No reward function is required for learning; an agent
only needs to be given a primary goal to achieve. Subgoals of a goal G from the
hierarchy are computed as descriptions of states, which if previously achieved
increase the total efficiency of the available policies for G. These state
descriptions are introduced as new sensor predicates into the rule language of
the agent, which allows for sensing important intermediate states and for
updating environment rules and policies accordingly.
