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Policy advice is a transfer learning method where a student agent is able tolearn faster via advice from a teacher. However, both this and otherreinforcement learning transfer methods have little theoretical analysis. Thispaper formally defines a setting where multiple teacher agents can provideadvice to a student and introduces an algorithm to leverage both autonomousexploration and teacher's advice. Our regret bounds justify the intuition thatgood teachers help while bad teachers hurt. Using our formalization, we arealso able to quantify, for the first time, when negative transfer can occurwithin such a reinforcement learning setting.

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