What mechanisms do AI agents use to adapt their behaviors in changing environments?

AI agents utilize several mechanisms for adapting their behaviors in response to changing environments. One common approach is reinforcement learning, which allows agents to learn optimal actions through trial and error based on feedback from their environment. Additionally, agents may leverage online learning techniques to update their models in real-time, incorporating new data as it becomes available. Other strategies include transfer learning, where knowledge gained from one task is applied to different but related tasks, and contextual bandits, which help agents optimize decisions by balancing exploration and exploitation in real-time.