Automatic Curriculum Generation Based on Expert Trajectory

Abstract

The application of curriculum learning in reinforcement learning is expected to improve the learning efficiency and performance of autonomous agents by affecting their behavior acquisition. However, there are many challenges in generating the curriculum necessary for curriculum learning, as it is a prerequisite for having expert knowledge and deep prior understanding. In addition, curriculum generation methods are often specialized to the target task and lack versatility. In this study, new method is anticipated to solve the above problem by automatically generating a curriculum using clustering based on expert trajectories (state history). In the experiments, we compared the learning efficiency of normal agents and agents using the proposed method, and confirmed that the proposed method improved the performance in all aspects.