  Multi-task Inverse Reinforcement Learning (IRL) is the problem of inferring
multiple reward functions from expert demonstrations. Prior work, built on
Bayesian IRL, is unable to scale to complex environments due to computational
constraints. This paper contributes a formulation of multi-task IRL in the more
computationally efficient Maximum Causal Entropy (MCE) IRL framework.
Experiments show our approach can perform one-shot imitation learning in a
gridworld environment that single-task IRL algorithms need hundreds of
demonstrations to solve. We outline preliminary work using meta-learning to
extend our method to the function approximator setting of modern MCE IRL
algorithms. Evaluating on multi-task variants of common simulated robotics
benchmarks, we discover serious limitations of these IRL algorithms, and
conclude with suggestions for further work.
