迈向具有情感智能和负责任的强化学习
Towards Emotionally Intelligent and Responsible Reinforcement Learning
AI总结：
针对医疗保健等领域个性化决策系统忽视情感背景与伦理约束的问题，提出负责任强化学习（RRL）框架，将情感理解与伦理考量整合到序列决策中，通过CMDP优化参与度与依从性并确保安全，连接多领域并为相关系统提供方法学基础。
摘要：
医疗保健和行为支持中的个性化决策系统通常依赖静态规则或最大化参与度的启发式方法，忽视用户的情感背景和伦理约束。此类方法可能推荐不敏感或不安全的干预措施，尤其在涉及严重精神疾病、物质使用障碍或抑郁症的领域。为解决这一局限，我们提出负责任强化学习（RRL）框架，将情感与情境理解及伦理考量整合到序列决策过程中。RRL将个性化表述为约束马尔可夫决策过程（CMDP），智能体在优化参与度与依从性的同时确保情感一致性和伦理安全。我们引入多目标奖励函数，明确平衡短期行为参与度与长期用户福祉，并定义情感感知状态表示，捕捉情感准备度、情感状态和风险的波动。该架构可通过任何强化学习算法（如DQN、PPO）结合安全约束或拉格朗日正则化实例化。从概念上，此框架在机器学习策略优化中实现同理心与责任，连接安全强化学习、情感计算和负责任AI。我们讨论该方法在行为健康、教育和数字治疗等以人为中心的领域的意义，并概述未来实证工作的基于模拟的验证路径。本文旨在开启关于伦理对齐强化学习的方法学对话，以构建具有情感意识和可信赖的个性化系统。
英文摘要：
Personalized decision systems in healthcare and behavioral support often rely on static rule-based or engagement-maximizing heuristics that overlook users' emotional context and ethical constraints. Such approaches risk recommending insensitive or unsafe interventions, especially in domains involving serious mental illness, substance use disorders, or depression. To address this limitation, we propose a Responsible Reinforcement Learning (RRL) framework that integrates emotional and contextual understanding with ethical considerations into the sequential decision-making process. RRL formulates personalization as a Constrained Markov Decision Process (CMDP), where the agent optimizes engagement and adherence while ensuring emotional alignment and ethical safety. We introduce a multi-objective reward function that explicitly balances short-term behavioral engagement with long-term user well-being, and define an emotion-informed state representation that captures fluctuations in emotional readiness, affect, and risk. The proposed architecture can be instantiated with any RL algorithm (e.g., DQN, PPO) augmented with safety constraints or Lagrangian regularization. Conceptually, this framework operationalizes empathy and responsibility within machine learning policy optimization, bridging safe RL, affective computing and responsible AI. We discuss the implications of this approach for human-centric domains such as behavioral health, education, and digital therapeutics, and outline simulation-based validation paths for future empirical work. This paper aims to initiate a methodological conversation about ethically aligned reinforcement learning for emotionally aware and trustworthy personalization systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
