Learning transitional skills with intrinsic motivation

Qiangxing Tian, Jinxin Liu, Donglin Wang

Abstract: By maximizing an information theoretic objective, a few recent methods empower the agent to explore the environment and learn useful skills without supervision. However, when considering to use multiple consecutive skills to complete a specific task, the transition from one to another cannot guarantee the success of the process due to the evident gap between skills. In this paper, we propose to learn transitional skills (LTS) in addition to creating diverse primitive skills without a reward function. By introducing an extra latent variable for transitional skills, our LTS method discovers both primitive and transitional skills by minimizing the difference of mutual information and the similarity of skills. By considering various simulated robotic tasks, our results demonstrate the effectiveness of LTS on learning both diverse primitive skills and transitional skills, and show its superiority in smooth transition of skills over the state-of-the-art baseline DIAYN.