oIRL: Robust Adversarial Inverse Reinforcement Learning with Temporally Extended Actions

20 Feb 2020David VenutoJhelum ChakravortyLeonard BoussiouxJunhao WangGavin McCrackenDoina Precup

Explicit engineering of reward functions for given environments has been a major hindrance to reinforcement learning methods. While Inverse Reinforcement Learning (IRL) is a solution to recover reward functions from demonstrations only, these learned rewards are generally heavily \textit{entangled} with the dynamics of the environment and therefore not portable or \emph{robust} to changing environments... (read more)

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