PEBBLE：通过重新标记经验和无监督预训练的反馈高效交互式强化学习
PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training
摘要：
将复杂的目标传递给强化学习（RL）代理通常是困难的，涉及到精心设计的奖励函数，这些函数既有足够的信息，又很容易提供。人在回路RL方法允许从业者通过定制的反馈来交互式地教导代理;然而，由于人类反馈非常昂贵，因此这种方法在规模上具有挑战性。在这项工作中，我们的目标是使这个过程更有效的样本和反馈。我们提出了一个离政策，交互式强化学习算法，利用反馈和离策略学习的优势。具体来说，我们学习的奖励模型，积极查询教师的偏好之间的两个剪辑的行为，并用它来训练代理。为了实现离策略学习，当代理的奖励模型改变时，我们重新标记代理的所有过去经验。我们还表明，预训练我们的代理与无监督的探索大大增加了其查询的里程。我们证明，我们的方法是能够学习任务的复杂性比以前考虑的人在循环的方法，包括各种运动和机器人操作技能。我们还表明，我们的方法能够利用实时的人类反馈来有效地防止奖励剥削，并学习难以用标准奖励函数指定的新行为。
英文摘要：
Conveying complex objectives to reinforcement learning (RL) agents can often be difficult, involving meticulous design of reward functions that are sufficiently informative yet easy enough to provide. Human-in-the-loop RL methods allow practitioners to instead interactively teach agents through tailored feedback; however, such approaches have been challenging to scale since human feedback is very expensive. In this work, we aim to make this process more sample- and feedback-efficient. We present an off-policy, interactive RL algorithm that capitalizes on the strengths of both feedback and off-policy learning. Specifically, we learn a reward model by actively querying a teacher's preferences between two clips of behavior and use it to train an agent. To enable off-policy learning, we relabel all the agent's past experience when its reward model changes. We additionally show that pre-training our agents with unsupervised exploration substantially increases the mileage of its queries. We demonstrate that our approach is capable of learning tasks of higher complexity than previously considered by human-in-the-loop methods, including a variety of locomotion and robotic manipulation skills. We also show that our method is able to utilize real-time human feedback to effectively prevent reward exploitation and learn new behaviors that are difficult to specify with standard reward functions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
