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.

This paper investigates control synthesis for motion planning under conditions of uncertainty, specifically in robot motion and environmental properties, which are modeled using a probabilistic labeled Markov decision process (PL-MDP). To address this, a model-free reinforcement learning (RL) approach is designed to produce a finite-memory control policy that meets complex tasks specified by linear temporal logic (LTL) formulas. Recognizing the presence of uncertainties and potentially conflicting objectives, this study centers on addressing infeasible LTL specifications. A relaxed LTL constraint enables the agent to adapt its motion plan, allowing for partial satisfaction by accounting for necessary task violations. Additionally, a new automaton structure is introduced to increase the density of accepting rewards, facilitating deterministic policy outcomes. The proposed RL framework is rigorously analyzed and prioritizes two key objectives: (1) satisfying the acceptance condition of the relaxed product MDP, and (2) minimizing long-term violation costs. Simulation and experimental results are presented to demonstrate the framework’s effectiveness and robustness.