标题： 将稳态与强化学习联系起来：动机行为的内部状态控制 显示英文标题

标题： Linking Homeostasis to Reinforcement Learning: Internal State Control of Motivated Behavior

摘要： 对于生物体来说，生存依赖于通过有动机的行为对内部生理状态进行有效调节。 从这一观点出发，我们提出了一种称为稳态调节强化学习（HRRL）的框架，用于描述通过学习的预测控制策略优化内部状态的生物代理，将生物学原理与计算学习相结合。 我们表明，HRRL本质上会产生多种行为，如风险规避、预期调节和适应性运动，这与观察到的生物学现象一致。 其在深度强化学习中的扩展使得自主探索、分层行为和潜在的实际机器人应用成为可能。 我们认为，HRRL为理解动机、学习和决策提供了生物上合理的基础，对人工智能、神经科学以及精神疾病的原因有广泛的影响，最终促进了我们对复杂环境中适应性行为的理解。 显示英文摘要

摘要： For living beings, survival depends on effective regulation of internal physiological states through motivated behaviors. In this perspective we propose that Homeostatically Regulated Reinforcement Learning (HRRL) as a framework to describe biological agents that optimize internal states via learned predictive control strategies, integrating biological principles with computational learning. We show that HRRL inherently produces multiple behaviors such as risk aversion, anticipatory regulation, and adaptive movement, aligning with observed biological phenomena. Its extension to deep reinforcement learning enables autonomous exploration, hierarchical behavior, and potential real-world robotic applications. We argue further that HRRL offers a biologically plausible foundation for understanding motivation, learning, and decision-making, with broad implications for artificial intelligence, neuroscience, and understanding the causes of psychiatric disorders, ultimately advancing our understanding of adaptive behavior in complex environments.