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Explicit engineering of reward functions for given environments has been amajor hindrance to reinforcement learning methods. While Inverse ReinforcementLearning (IRL) is a solution to recover reward functions from demonstrationsonly, these learned rewards are generally heavily \textit{entangled} with thedynamics of the environment and therefore not portable or \emph{robust} tochanging environments. Modern adversarial methods have yielded some success inreducing reward entanglement in the IRL setting. In this work, we leverage onesuch method, Adversarial Inverse Reinforcement Learning (AIRL), to propose analgorithm that learns hierarchical disentangled rewards with a policy overoptions. We show that this method has the ability to learn \emph{generalizable}policies and reward functions in complex transfer learning tasks, whileyielding results in continuous control benchmarks that are comparable to thoseof the state-of-the-art methods.

oIRL: Robust Adversarial Inverse Reinforcement Learning with Temporally Extended Actions