Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations

12 Apr 2019Daniel S. BrownWonjoon GooPrabhat NagarajanScott Niekum

A critical flaw of existing inverse reinforcement learning (IRL) methods is their inability to significantly outperform the demonstrator. This is because IRL typically seeks a reward function that makes the demonstrator appear near-optimal, rather than inferring the underlying intentions of the demonstrator that may have been poorly executed in practice... (read more)

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