This paper introduces a novel explanation model for reinforcement learning agents, grounded in Aristotle’s practical syllogism, with the goal of enhancing human-friendly explanations (HFEs) in urban intelligent design. By aligning the internal processes of a Q-learning agent with Aristotelian categories such as telos, phronēsis, and boulesis, the model enables the interpretation of agent behavior as a form of ethically structured practical reasoning. The selected case study focuses on an urban planning agent designed to optimize service accessibility and spatial coherence in real city environments. Through this alignment, the agent’s policy learning, state-action evaluation, and reward optimization are made intelligible to human users in terms of goals, normative principles, perception, deliberation, and action. Evaluated using the Human-Friendly Explanations (HFE) checklist, the model exhibits strengths in interpretability, comprehensibility, and ethical relevance, while identifying areas for improvement such as contrastive reasoning, personalization, and regulatory compliance. This work offers a conceptual and methodological foundation for integrating philosophical models into Explainable Reinforcement Learning (XRL), facilitating transparent, ethical, and user-aligned AI systems. Future directions include empirical validation, interactive implementation, and domain-specific adaptation across sociotechnical contexts.