自主机器的自激励目标导向强化学习：一种简短综述
Autotelic Agents with Intrinsically Motivated Goal-Conditioned Reinforcement Learning: a Short Survey
AI总结：
本文提出了一种基于目标导向强化学习的发展性强化学习框架，旨在解决自激励技能获取问题，并探讨了目标表示和优先级学习的挑战。
摘要：
构建能够探索开放环境、发现可能交互并建立技能库的自主机器，是人工智能的普遍目标。发展性方法认为，只有通过自激励学习代理才能实现这一目标，这些代理能够学习表示、生成、选择和解决自身问题。近年来，发展性方法与深度强化学习（RL）方法的融合正催生出一个新的领域：发展性强化学习。发展性强化学习关注使用深度RL算法解决发展性问题——即自激励获取开放性技能库。目标的自生成需要学习紧凑的目标编码及其相关的目标达成函数。这与标准RL算法不同，标准算法最初设计用于通过外部奖励信号解决预定义的目标集。本文介绍了发展性强化学习，并提出了一种基于目标导向RL的计算框架来解决自激励技能获取问题。接着，本文介绍了文献中各种目标表示的类型，然后回顾了现有方法以学习在自主系统中表示和优先考虑目标。最后，本文通过讨论在追求自激励技能获取过程中的一些开放性挑战来结束。
英文摘要：
Building autonomous machines that can explore open-ended environments, discover possible interactions and build repertoires of skills is a general objective of artificial intelligence. Developmental approaches argue that this can only be achieved by $autotelic$ $agents$: intrinsically motivated learning agents that can learn to represent, generate, select and solve their own problems. In recent years, the convergence of developmental approaches with deep reinforcement learning (RL) methods has been leading to the emergence of a new field: $developmental$ $reinforcement$ $learning$. Developmental RL is concerned with the use of deep RL algorithms to tackle a developmental problem -- the $intrinsically$ $motivated$ $acquisition$ $of$ $open$-$ended$ $repertoires$ $of$ $skills$. The self-generation of goals requires the learning of compact goal encodings as well as their associated goal-achievement functions. This raises new challenges compared to standard RL algorithms originally designed to tackle pre-defined sets of goals using external reward signals. The present paper introduces developmental RL and proposes a computational framework based on goal-conditioned RL to tackle the intrinsically motivated skills acquisition problem. It proceeds to present a typology of the various goal representations used in the literature, before reviewing existing methods to learn to represent and prioritize goals in autonomous systems. We finally close the paper by discussing some open challenges in the quest of intrinsically motivated skills acquisition.
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。
