Human Engagement Providing Evaluative and Informative Advice for Interactive Reinforcement Learning
This addresses the problem of optimizing human-agent interaction for faster learning in reinforcement learning, though it is incremental as it builds on prior research on human-sourced advice.
This work investigated whether humans prefer to give evaluative or informative advice to interactive reinforcement learning agents, finding that informative advice leads to more accurate guidance, longer human engagement, and higher perceived effectiveness.
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.