Human-in-the-Loop Reinforcement Learning: A Survey and Position on Requirements, Challenges, and Opportunities Article Swipe
Carl Orge Retzlaff
,
Srijita Das
,
Christabel Wayllace
,
Payam Mousavi
,
Mohammad Afshari
,
Tianpei Yang
,
Anna Saranti
,
Alessa Angerschmid
,
Matthew E. Taylor
,
Andreas Holzinger
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1613/jair.1.15348
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1613/jair.1.15348
Artificial intelligence (AI) and especially reinforcement learning (RL) have the potential to enable agents to learn and perform tasks autonomously with superhuman performance. However, we consider RL as fundamentally a Human-in-the-Loop (HITL) paradigm, even when an agent eventually performs its task autonomously. In cases where the reward function is challenging or impossible to define, HITL approaches are considered particularly advantageous. The application of Reinforcement Learning from Human Feedback (RLHF) in systems such as ChatGPT demonstrates the effectiveness of optimizing for user experience and integrating their feedback into the training loop. In HITL RL, human input is integrated during the agent’s learning process, allowing iterative updates and fine-tuning based on human feedback, thus enhancing the agent’s performance. Since the human is an essential part of this process, we argue that human-centric approaches are the key to successful RL, a fact that has not been adequately considered in the existing literature. This paper aims to inform readers about current explainability methods in HITL RL. It also shows how the application of explainable AI (xAI) and specific improvements to existing explainability approaches can enable a better human-agent interaction in HITL RL for all types of users, whether for lay people, domain experts, or machine learning specialists. Accounting for the workflow in HITL RL and based on software and machine learning methodologies, this article identifies four phases for human involvement for creating HITL RL systems: (1) Agent Development, (2) Agent Learning, (3) Agent Evaluation, and (4) Agent Deployment. We highlight human involvement, explanation requirements, new challenges, and goals for each phase. We furthermore identify low-risk, high-return opportunities for explainability research in HITL RL and present long-term research goals to advance the field. Finally, we propose a vision of human-robot collaboration that allows both parties to reach their full potential and cooperate effectively.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1613/jair.1.15348
- https://jair.org/index.php/jair/article/download/15348/27006
- OA Status
- diamond
- Cited By
- 84
- References
- 197
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391399751
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391399751Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1613/jair.1.15348Digital Object Identifier
- Title
-
Human-in-the-Loop Reinforcement Learning: A Survey and Position on Requirements, Challenges, and OpportunitiesWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-30Full publication date if available
- Authors
-
Carl Orge Retzlaff, Srijita Das, Christabel Wayllace, Payam Mousavi, Mohammad Afshari, Tianpei Yang, Anna Saranti, Alessa Angerschmid, Matthew E. Taylor, Andreas HolzingerList of authors in order
- Landing page
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https://doi.org/10.1613/jair.1.15348Publisher landing page
- PDF URL
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https://jair.org/index.php/jair/article/download/15348/27006Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://jair.org/index.php/jair/article/download/15348/27006Direct OA link when available
- Concepts
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Human-in-the-loop, Reinforcement learning, Position (finance), Loop (graph theory), Computer science, Reinforcement, Artificial intelligence, Business, Psychology, Mathematics, Social psychology, Combinatorics, FinanceTop concepts (fields/topics) attached by OpenAlex
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84Total citation count in OpenAlex
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2025: 56, 2024: 28Per-year citation counts (last 5 years)
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197Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| cited_by_percentile_year.min | 99 |
| countries_distinct_count | 4 |
| institutions_distinct_count | 10 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/17 |
| sustainable_development_goals[0].score | 0.4099999964237213 |
| sustainable_development_goals[0].display_name | Partnerships for the goals |
| citation_normalized_percentile.value | 0.99859028 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |