  We present an algorithm for Inverse Reinforcement Learning (IRL) from expert
state observations only. Our approach decouples reward modelling from policy
learning, unlike state-of-the-art adversarial methods which require updating
the reward model during policy search and are known to be unstable and
difficult to optimize. Our method, IL-flOw, recovers the expert policy by
modelling state-state transitions, by generating rewards using deep density
estimators trained on the demonstration trajectories, avoiding the instability
issues of adversarial methods. We demonstrate that using the state transition
log-probability density as a reward signal for forward reinforcement learning
translates to matching the trajectory distribution of the expert
demonstrations, and experimentally show good recovery of the true reward signal
as well as state of the art results for imitation from observation on
locomotion and robotic continuous control tasks.
