- Title
- Human engagement providing evaluative and informative advice for interactive reinforcement learning
- Creator
- Bignold, Adam; Cruz, Francisco; Dazeley, Richard; Vamplew, Peter; Foale, Cameron
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/197965
- Identifier
- vital:18966
- Identifier
-
https://doi.org/10.1007/s00521-021-06850-6
- Identifier
- ISSN:0941-0643 (ISSN)
- Abstract
- 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. © 2022, The Author(s).
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Relation
- Neural Computing and Applications Vol. 35, no. 25 (2023), p. 18215-18230
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- http://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright © 2022, The Author(s)
- Rights
- Open Access
- Subject
- 4602 Artificial intelligence; 4603 Computer vision and multimedia computation; 4611 Machine learning Assisted reinforcement learning; Evaluative and informative advice; Interactive reinforcement learning; Policy shaping; Reward shaping; User study
- Full Text
- Reviewed
- Funder
- This work has been partially supported by the Australian Government Research Training Program (RTP) and the RTP Fee-Offset Scholarship through Federation University Australia.
- Hits: 803
- Visitors: 808
- Downloads: 8
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Published version | 2 MB | Adobe Acrobat PDF | View Details Download |