论文标题
互动限制的逆增强学习
Interaction-limited Inverse Reinforcement Learning
论文作者
论文摘要
本文提出了一个逆增强学习（IRL）框架，以加速学习时学习者\ textit {互动}是培训期间的\ textit {limited}。我们的设置是由没有可用的老师或老师无法访问学生学习动力的现实情况的动机。我们提出了两种不同的培训策略：涵盖教师的观点的课程逆增强学习（CIRL），以及以学习者的观点为重点的自进度逆强化学习（SPIRL）。使用实际机器人从人类演示者中学习任务的实验和实验中的实验，我们表明我们的训练策略可以比随机的老师更快地训练训练，而不是cirl的随机老师，而Spirl的批处理学习者则可以允许培训。
This paper proposes an inverse reinforcement learning (IRL) framework to accelerate learning when the learner-teacher \textit{interaction} is \textit{limited} during training. Our setting is motivated by the realistic scenarios where a helpful teacher is not available or when the teacher cannot access the learning dynamics of the student. We present two different training strategies: Curriculum Inverse Reinforcement Learning (CIRL) covering the teacher's perspective, and Self-Paced Inverse Reinforcement Learning (SPIRL) focusing on the learner's perspective. Using experiments in simulations and experiments with a real robot learning a task from a human demonstrator, we show that our training strategies can allow a faster training than a random teacher for CIRL and than a batch learner for SPIRL.
