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State-only Imitation with Transition Dynamics Mismatch
[article]
2020
arXiv
pre-print
Imitation Learning (IL) is a popular paradigm for training agents to achieve complicated goals by leveraging expert behavior, rather than dealing with the hardships of designing a correct reward function. With the environment modeled as a Markov Decision Process (MDP), most of the existing IL algorithms are contingent on the availability of expert demonstrations in the same MDP as the one in which a new imitator policy is to be learned. This is uncharacteristic of many real-life scenarios where
arXiv:2002.11879v1
fatcat:y2jz7sdqdzcrvab7wwxjqey3km