Repeated Inverse Reinforcement Learning

Part of Advances in Neural Information Processing Systems 30 (NIPS 2017)

Authors

Kareem Amin, Nan Jiang, Satinder Singh

Abstract

We introduce a novel repeated Inverse Reinforcement Learning problem: the agent has to act on behalf of a human in a sequence of tasks and wishes to minimize the number of tasks that it surprises the human by acting suboptimally with respect to how the human would have acted. Each time the human is surprised, the agent is provided a demonstration of the desired behavior by the human. We formalize this problem, including how the sequence of tasks is chosen, in a few different ways and provide some foundational results.