@article{Xiao2026, 
author = {Shaoping Xiao and Zhaoan Wang and Junchao Li and Caden Noeller and Jiefeng Jiang and Jun Wang},
title = {Implementation of Human-AI Interaction in Reinforcement Learning: Literature Review and Case Studies},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {86},
number = {2},
pages = {1-62},
keywords = {Human-AI interaction, reinforcement learning, partially observable environments, trust model, ethical constraints},
url = {https://www.sciopen.com/article/10.32604/cmc.2025.072146},
doi = {10.32604/cmc.2025.072146},
abstract = {The integration of human factors into artificial intelligence (AI) systems has emerged as a critical research frontier, particularly in reinforcement learning (RL), where human-AI interaction (HAII) presents both opportunities and challenges. As RL continues to demonstrate remarkable success in model-free and partially observable environments, its real-world deployment increasingly requires effective collaboration with human operators and stakeholders. This article systematically examines HAII techniques in RL through both theoretical analysis and practical case studies. We establish a conceptual framework built upon three fundamental pillars of effective human-AI collaboration: computational trust modeling, system usability, and decision understandability. Our comprehensive review organizes HAII methods into five key categories: (1) learning from human feedback, including various shaping approaches; (2) learning from human demonstration through inverse RL and imitation learning; (3) shared autonomy architectures for dynamic control allocation; (4) human-in-the-loop querying strategies for active learning; and (5) explainable RL techniques for interpretable policy generation. Recent state-of-the-art works are critically reviewed, with particular emphasis on advances incorporating large language models in human-AI interaction research. To illustrate some concepts, we present three detailed case studies: an empirical trust model for farmers adopting AI-driven agricultural management systems, the implementation of ethical constraints in robotic motion planning through human-guided RL, and an experimental investigation of human trust dynamics using a multi-armed bandit paradigm. These applications demonstrate how HAII principles can enhance RL systems’ practical utility while bridging the gap between theoretical RL and real-world human-centered applications, ultimately contributing to more deployable and socially beneficial intelligent systems.}
}