让智能体自己设定目标，自动学习新技能。
Autotelic Reinforcement Learning: Exploring Intrinsic Motivations for Skill Acquisition in Open-Ended Environments
- 基于内在动机设计自驱动学习框架，不依赖外部奖励。
- 提出评估探索、泛化与鲁棒性的多种指标。
- 适合研究自主智能体与持续学习的学者。
本文系统探讨了自驱强化学习（autotelic RL），强调内在动机在开放环境中技能库自发形成中的作用。区分了基于知识与基于能力的内在动机，阐明其如何支撑自主智能体生成并追求自我定义的目标。文中深入分析了内在动机目标探索过程（IMGEPs）的类型，聚焦于多目标强化学习与发展型机器人应用。将自驱学习问题建模为无奖励的马尔可夫决策过程（MDP），要求智能体自主表征、生成并掌握自身目标。针对此类智能体的评估难题，提出多种衡量探索性、泛化能力与鲁棒性的指标。本研究旨在深化对自驱学习智能体的理解，推动其在多样化动态环境中的技能获取能力。
原文摘要 · Abstract (English)
This paper presents a comprehensive overview of autotelic Reinforcement Learning (RL), emphasizing the role of intrinsic motivations in the open-ended formation of skill repertoires. We delineate the distinctions between knowledge-based and competence-based intrinsic motivations, illustrating how these concepts inform the development of autonomous agents capable of generating and pursuing self-defined goals. The typology of Intrinsically Motivated Goal Exploration Processes (IMGEPs) is explored, with a focus on the implications for multi-goal RL and developmental robotics. The autotelic learning problem is framed within a reward-free Markov Decision Process (MDP), WHERE agents must autonomously represent, generate, and master their own goals. We address the unique challenges in evaluating such agents, proposing various metrics for measuring exploration, generalization, and robustness in complex environments. This work aims to advance the understanding of autotelic RL agents and their potential for enhancing skill acquisition in a diverse and dynamic setting.
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