IL-flOw: Imitation Learning from Observation using Normalizing Flows. (arXiv:2205.09251v1 [cs.LG])

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 …

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