Imitation learning enables robots to learn from demonstrations. Previous imitation learning algorithms usually assume access to optimal expert demonstrations. However, in many real-world applications, this assumption is limiting. Most collected demonstrations are not optimal or are produced by an agent with slightly different dynamics. We therefore address the problem of imitation learning when the demonstrations can be sub-optimal or be drawn from agents with varying dynamics. We develop a metric composed of a feasibility score and an optimality score to measure how useful a demonstration is for imitation learning. The proposed score enables learning from more informative demonstrations, and disregarding the less relevant demonstrations. Our experiments on four environments in simulation and on a real robot show improved learned policies with higher expected return.

中文翻译：

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向动力学变化的代理商学习不完美的示范

模仿学习使机器人可以从演示中学习。以前的模仿学习算法通常假定可以访问最佳专家演示。但是，在许多实际应用中，这种假设是有局限性的。大多数收集的演示不是最佳的，或者是由动态稍有不同的代理产生的。因此，当演示可能不是最佳选择或从具有不同动态的特工中汲取灵感时，我们将解决模仿学习的问题。我们开发了一个由可行性评分和最优评分组成的指标，以衡量演示对模仿学习的实用性。拟议的分数可以从更多翔实的示范中学习，而无视不太相关的示范。