Integrating symbolic knowledge in reinforcement learning

Abstract

A tabula rasa learning technique has worked well in well defined grid like problems (Barto et al., 1993). Nevertheless, it has severe limitations when applied in complex domains. In order to build a true learning system in complex domains, we have to begin to integrate a considerable amount of bias with the learner that has the ability to adapt a priori knowledge. This bias can assume a variety of forms. In this paper, in addition to reflex rules (Millan, 1996), symbolic knowledge about the environment is embedded into the learning system (Kaelbling, 1993). The incorporation of such knowledge aids the learner to identify and split key states rapidly. The learner is tested on a B21 robot for a goal reaching task. Experimental results show that after few trials the robot has indeed learned to unfold its path and to consistently follow the shortest path to the goal.