  Adversarial Imitation Learning alternates between learning a discriminator --
which tells apart expert's demonstrations from generated ones -- and a
generator's policy to produce trajectories that can fool this discriminator.
This alternated optimization is known to be delicate in practice since it
compounds unstable adversarial training with brittle and sample-inefficient
reinforcement learning. We propose to remove the burden of the policy
optimization steps by leveraging a novel discriminator formulation.
Specifically, our discriminator is explicitly conditioned on two policies: the
one from the previous generator's iteration and a learnable policy. When
optimized, this discriminator directly learns the optimal generator's policy.
Consequently, our discriminator's update solves the generator's optimization
problem for free: learning a policy that imitates the expert does not require
an additional optimization loop. This formulation effectively cuts by half the
implementation and computational burden of Adversarial Imitation Learning
algorithms by removing the Reinforcement Learning phase altogether. We show on
a variety of tasks that our simpler approach is competitive to prevalent
Imitation Learning methods.
