Behavioural cloning has been extensively used to train agents and is
recognized as a fast and solid approach to teach general behaviours based on
expert trajectories. Such method follows the supervised learning paradigm and
it strongly depends on the distribution of the data. In our paper, we show how
combining behavioural cloning with human-in-the-loop training solves some of
its flaws and provides an agent task-specific corrections to overcome tricky
situations while speeding up the training time and lowering the required
resources. To do this, we introduce a novel approach that allows an expert to
take control of the agent at any moment during a simulation and provide optimal
solutions to its problematic situations. Our experiments show that this
approach leads to better policies both in terms of quantitative evaluation and
in human-likeliness.

本文介绍了如何将行为克隆与人在环环学习相结合，利用一种新方法在模拟中允许专家随时控制代理并提供最优解，从而解决了行为克隆中的一些缺陷，提高了训练效率和降低了所需资源，实验表明该方法在定量评估和人类相似性方面都具有更好的效果。