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Thios podcast discusses the IBM article by Cole Stryker entitled "How do AI agents learn and adapt over time?"

AI agent learning is the process where an artificial intelligence agent improves performance through interaction with its environment and data. While some AI agents are reactive and do not learn, learning agents adapt based on feedback, enhancing decision-making in dynamic situations.

Learning agents typically consist of four key components: a performance element, learning element, critic, and problem generator. The process is underpinned by machine learning, with core techniques including supervised, unsupervised, and reinforcement learning, which utilise various feedback mechanisms to refine the agent's behaviour over time.

Additionally, the podcast touches upon concepts like continuous learning and multiagent learning, where agents learn collaboratively or competitively.