Incorporating homeostasis into reinforcement learning improves artificial intelligence models.

Scientists building AI robots apply homeostatically regulated reinforcement learning to help machines adapt to changing needs, like energy levels or damage. This approach lets robots decide better when to recharge or rest, similar to living beings. It improves the flexibility and survival of AI agents in complex environments.