Learning Options From Demonstrations: APac-ManCase Study.

Reinforcement learning (RL) is a machine learning paradigm behind many successes in games, robotics, and control applications. RL agents improve through trial-and-error, therefore undergoing a learning phase during which they perform suboptimally. Research effort has been put into optimizing behavior during this period, to reduce its duration and to maximize after-learning performance. We introduc...