Agents Teaching Agents in Reinforcement Learning (Nectar Abstract)

摘要

Using reinforcement learning(RL), agents can autonomously learn a control policy to master sequential-decision tasks. Rather than always learning tabula rasa, our recent work considers how an experienced RL agent, the teacher, can help another RL agent, the student, to learn. As a motivating example, consider a household robot that has learned to perform tasks in a household. When the consumer purchases a new robot, she would like the student robot to quickly learn to perform the same tasks as the teacher robot, even if the new robot has different state representation, learning method, or manufacturer. Our goals are to: 1) Allow the student to learn faster with the teacher than without it, 2) Allow the student and teacher to have different learning methods and knowledge representations, 3) Not limit the student's performance when the teacher is sub-optimal, 4) Not require a complex, shared language, and 5) Limit the amount of communication required between the agents.