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We present an algorithm for Inverse Reinforcement Learning (IRL) from expertstate observations only. Our approach decouples reward modelling from policylearning, unlike state-of-the-art adversarial methods which require updatingthe reward model during policy search and are known to be unstable anddifficult to optimize. Our method, IL-flOw, recovers the expert policy bymodelling state-state transitions, by generating rewards using deep densityestimators trained on the demonstration trajectories, avoiding the instabilityissues of adversarial methods. We demonstrate that using the state transitionlog-probability density as a reward signal for forward reinforcement learningtranslates to matching the trajectory distribution of the expertdemonstrations, and experimentally show good recovery of the true reward signalas well as state of the art results for imitation from observation onlocomotion and robotic continuous control tasks.

IL-flOw: Imitation Learning from Observation using Normalizing Flows