  Often times in imitation learning (IL), the environment we collect expert
demonstrations in and the environment we want to deploy our learned policy in
aren't exactly the same (e.g. demonstrations collected in simulation but
deployment in the real world). Compared to policy-centric approaches to IL like
behavioural cloning, reward-centric approaches like inverse reinforcement
learning (IRL) often better replicate expert behaviour in new environments.
This transfer is usually performed by optimising the recovered reward under the
dynamics of the target environment. However, (a) we find that modern deep IL
algorithms frequently recover rewards which induce policies far weaker than the
expert, even in the same environment the demonstrations were collected in.
Furthermore, (b) these rewards are often quite poorly shaped, necessitating
extensive environment interaction to optimise effectively. We provide simple
and scalable fixes to both of these concerns. For (a), we find that reward
model ensembles combined with a slightly different training objective
significantly improves re-training and transfer performance. For (b), we
propose a novel evolution-strategies based method EvIL to optimise for a
reward-shaping term that speeds up re-training in the target environment,
closing a gap left open by the classical theory of IRL. On a suite of
continuous control tasks, we are able to re-train policies in target (and
source) environments more interaction-efficiently than prior work.
