Autonomous identification, categorization and generalization of policies based on task type

A life-long learning agent must have the ability to learn new tasks, adapt the policies of already learned tasks, and extract and reuse knowledge from previous tasks for future use. To do the latter, it needs methods that can autonomously identify, categorize and generalize control and representational knowledge. This paper presents a novel approach to achieve this by combining the policy homomorphism framework with a utility criterion to autonomously identify task types, categorize situation-specific policy instances into these types, and generalize the policies into a single abstract policy for each identified task type. The capabilities of this approach to identify, categorize, and generalize skills, as well as the potential benefit of reuse of the abstracted policies for the learning of new tasks is demonstrated in a grid world domain.