  We present the ADaptive Adversarial Imitation Learning (ADAIL) algorithm for
learning adaptive policies that can be transferred between environments of
varying dynamics, by imitating a small number of demonstrations collected from
a single source domain. This is an important problem in robotic learning
because in real world scenarios 1) reward functions are hard to obtain, 2)
learned policies from one domain are difficult to deploy in another due to
varying source to target domain statistics, 3) collecting expert demonstrations
in multiple environments where the dynamics are known and controlled is often
infeasible. We address these constraints by building upon recent advances in
adversarial imitation learning; we condition our policy on a learned dynamics
embedding and we employ a domain-adversarial loss to learn a dynamics-invariant
discriminator. The effectiveness of our method is demonstrated on simulated
control tasks with varying environment dynamics and the learned adaptive agent
outperforms several recent baselines.
