Interpretable Reinforcement Learning with Multilevel Subgoal Discovery
This addresses the challenge of interpretability and efficiency in reinforcement learning for AI researchers, though it appears incremental as it builds on existing hierarchical RL concepts.
The paper tackles the problem of interpretable reinforcement learning in discrete environments by proposing a model that discovers multilevel subgoal hierarchies without requiring reward functions, achieving improved policy efficiency through probabilistic rule learning.
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.