人的参与为交互式强化学习提供评估和信息建议
Human Engagement Providing Evaluative and Informative Advice for Interactive Reinforcement Learning
摘要：
交互式强化学习提出使用外部来源的信息来加速学习过程。当与学习者代理交互时，人类可以提供评价性或信息性的建议。之前的研究主要集中在人工建议的效果上，包括对交互式强化学习过程的实时反馈，特别是旨在提高智能体的学习速度，同时最大限度地减少对人类的时间需求。这项工作的重点是回答这两种方法，评估或信息，是人类的首选教学方法。此外，这项工作提出了一个实验装置，旨在比较人们使用的方法来提供建议，在人类参与方面的人类试验。所获得的结果表明，用户提供翔实的建议，学习者代理提供更准确的建议，愿意帮助学习者代理更长的时间，并提供更多的建议，每集。此外，自我评价的参与者使用的信息的方法表明，代理人的能力，遵循的建议是较高的，因此，他们觉得自己的建议是更高的准确性相比，人们提供的评价性建议。
英文摘要：
Interactive reinforcement learning proposes the use of externally-sourced information in order to speed up the learning process. When interacting with a learner agent, humans may provide either evaluative or informative advice. Prior research has focused on the effect of human-sourced advice by including real-time feedback on the interactive reinforcement learning process, specifically aiming to improve the learning speed of the agent, while minimising the time demands on the human. This work focuses on answering which of two approaches, evaluative or informative, is the preferred instructional approach for humans. Moreover, this work presents an experimental setup for a human-trial designed to compare the methods people use to deliver advice in terms of human engagement. The results obtained show that users giving informative advice to the learner agents provide more accurate advice, are willing to assist the learner agent for a longer time, and provide more advice per episode. Additionally, self-evaluation from participants using the informative approach has indicated that the agent's ability to follow the advice is higher, and therefore, they feel their own advice to be of higher accuracy when compared to people providing evaluative advice.
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。
