  Extrapolating beyond-demonstrator (BD) performance through the imitation
learning (IL) algorithm aims to learn from and subsequently outperform the
demonstrator. To that end, a representative approach is to leverage inverse
reinforcement learning (IRL) to infer a reward function from demonstrations
before performing RL on the learned reward function. However, most existing
reward extrapolation methods require massive demonstrations, making it
difficult to be applied in tasks of limited training data. To address this
problem, one simple solution is to perform data augmentation to artificially
generate more training data, which may incur severe inductive bias and policy
performance loss. In this paper, we propose a novel meta learning-based reward
extrapolation (MLRE) algorithm, which can effectively approximate the
ground-truth rewards using limited demonstrations. More specifically, MLRE
first learns an initial reward function from a set of tasks that have abundant
training data. Then the learned reward function will be fine-tuned using data
of the target task. Extensive simulation results demonstrated that the proposed
MLRE can achieve impressive performance improvement as compared to other
similar BDIL algorithms.
