Information for Imitation learning

Basic information

Associated people:

Associated organizations:


The idea with imitation learning is to train agents by imitating human behavior. If the agent imitates the human sufficiently well, its behavior would be aligned. See ยง7.1 of this paper for more information.

Goals of the agenda

With imitation learning, I think the goal is to produce aligned behavior that is as capable as a human (but not more capable). I think this corresponds to the first step of iterated distillation and amplification, namely distilling the human behavior.

Assumptions the agenda makes

AI timelines

No assumptions as far as I can tell.

Nature of intelligence



Title Publication date Author Publisher Affected organizations Affected people Affected agendas Notes
Scalable agent alignment via reward modeling: a research direction 2018-11-19 Jan Leike, David Krueger, Tom Everitt, Miljan Martic, Vishal Maini, Shane Legg arXiv Google DeepMind Recursive reward modeling, Imitation learning, inverse reinforcement learning, Cooperative inverse reinforcement learning, myopic reinforcement learning, iterated amplification, debate This paper introduces the (recursive) reward modeling agenda, discussing its basic outline, challenges, and ways to overcome those challenges. The paper also discusses alternative agendas and their relation to reward modeling.