Visualizing Mental Learning Processes with Invisible Mazes for Continuous Learning

This paper presents the way to design the continuous learning process model based on general reinforcement learning framework for both a human and a learning agent. The objective of this research is to bring the learning ability of the learning agent close to that of a human. We focus on both the reinforcement learning framework for the learning agent and the continuous learning model of a human. However, there are two kinds of questions. First question is how to bridge an enormous gap between them. To fill in the missing piece of reinforcement learning whose learning process is mainly behavior change, we add two mental learning processes, awareness as pre-learning process and reflection as post-learning process. Second question is how to observe mental learning processes of a human. Previous methods of human learning researches mostly depend on observable behaviors or activities. On the other hand, a learning process of a human has a major difficulty in observing since it is a mental process. Then a human learning process is yet-to-be-defined. So it is necessary to add a new twist to observe the learning process of a human. To solve this, we propose a new method for visualizing mental learning processes with invisible mazes consisting of invisible walls which are perceived as a sign that is the number of walls in the neighborhood. Besides, we add meta-actions for expressing and summarizing something to be aware of learning from mistake or to be reflected on learning from experience. A learner can mark up his/her sign-action traces by meta-actions for future success. It turns out to visualize his/her mental learning processes. This paper reports our learning support system for a human learner to visualize his/her mental learning processes with invisible mazes for continuous learning.