Analyzing human's continuous learning ability with the reflection cost

Abstract

This paper reports our latest experimental results on analyzing human's continuous learning ability with the reflection cost. To fill in the missing piece of reinforcement learning framework for the learning robot, we focus on two human mental learning processes, awareness as pre-learning process and reflection as post-learning process. To observe mental learning processes of a human, we propose a new method for visualizing them by the reflection subtask with invisible mazes. In our previous work, there is a strong negative correlation between the number of continuous learning stages and the reflection cost. It suggests that the continuous learner performs a very good job of the reflection subtask. To examine the reason why the non-continuous learner stops learning the task, we analyze the learner's performance of both the main learning task by achievement cost and the reflection subtask by reflection cost in each learning stage. As the experimental results, the reflection cost of the continuous learner is stable during the learning stages as compared to non-continuous learners. It suggests that the continuous learner can perform the reflection subtask in a certain amount of time, even though it becomes more difficult as the learning stage progressed.