智能体在生存优化中自发形成世界模型与探索行为
Emergence of Implicit World Models from Mortal Agents
- 以稳态维持为外部动机，驱动智能体自主演化
- 通过元强化学习机制，隐式构建环境模型并主动探索
- 适合研究具身智能与自驱学习的学者参考
我们探讨了在自主智能体的开放性行为优化过程中，世界模型与主动探索作为涌现属性的可能性。从理论生物学与人工生命的机械论视角出发，重点分析稳态作为自主智能体的开放性目标及通用整合型外在动机的潜力。随后，我们提出一种假设架构，通过结合元强化学习，使网络内部动态实现世界模型的隐式获取与主动探索，将领域适应视为达成鲁棒稳态的系统机制。
原文摘要 · Abstract (English)
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.
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