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.

翻译：行为克隆被广泛用于培训剂,被公认为一种基于专家轨迹教授一般行为的快速和坚实的方法,这种方法遵循受监督的学习范式,并在很大程度上取决于数据的分布。在我们的论文中,我们展示行为克隆与流动中的人的培训相结合如何解决其缺陷,并提供针对代理人任务的具体纠正,以克服棘手情况,同时加快培训时间和降低所需资源。为此,我们引入了一种新颖的方法,允许专家在模拟期间随时控制代理人,并为有问题的情况提供最佳解决办法。我们的实验表明,从数量评估和人性角度而言,这种做法导致更好的政策。