Theoretically-Grounded Policy Advice from Multiple Teachers in Reinforcement Learning Settings with Applications to Negative Transfer

Understand

Policy advice is a transfer learning method where a student agent is able to learn faster via advice from a teacher.

• However, both this and other reinforcement learning transfer methods have little theoretical analysis.
• This paper formally defines a setting where multiple teacher agents can provide advice to a student and introduces an algorithm to leverage both autonomous exploration and teacher's advice.
• Our regret bounds justify the intuition that good teachers help while bad teachers hurt.