We discuss the possibility of world models and active exploration as emergent properties of open-ended behavior optimization in autonomous agents. In discussing the source of the open-endedness of living things, we start from the perspective of biological systems as understood by the mechanistic approach of theoretical biology and artificial life. From this perspective, we discuss the potential of homeostasis in particular as an open-ended objective for autonomous agents and as a general, integrative extrinsic motivation. We then discuss the possibility of implicitly acquiring a world model and active exploration through the internal dynamics of a network, and a hypothetical architecture for this, by combining meta-reinforcement learning, which assumes domain adaptation as a system that achieves robust homeostasis.

翻译：我们探讨了世界模型与主动探索作为自主智能体在开放式行为优化过程中涌现特性的可能性。在讨论生命体开放性的来源时，我们从理论生物学与人工生命的机制主义视角出发，理解生物系统。基于这一视角，我们特别探讨了稳态作为自主智能体开放式目标的潜力，以及其作为普适性、整合性外在动机的可能性。随后，我们讨论了通过网络内部动力学隐式获取世界模型并进行主动探索的可能性，并提出一种假设性架构：通过结合元强化学习（该框架将领域适应视为实现鲁棒稳态的系统）来实现这一目标。