Which AI approach learns from interaction and adapts its strategies based on feedback received?

Reinforcement learning is an AI approach that focuses on learning through interaction with an environment. In this framework, an agent takes actions within a given environment and receives feedback in the form of rewards or penalties based on those actions. The agent's objective is to maximize cumulative rewards over time. This process allows the agent to adapt its strategies dynamically, learning from the consequences of its actions to improve future performance.

This method is particularly effective in situations where direct supervision is not feasible, and the agent must explore different strategies to determine the best course of action. Unlike supervised learning, which relies on labeled datasets to guide the training process, reinforcement learning emphasizes the importance of feedback from the environment, making it a powerful technique in areas such as game playing, robotics, and autonomous systems.

Supervised learning and unsupervised learning do not inherently involve interaction and feedback mechanisms in the same way that reinforcement learning does. Supervised learning requires a labeled dataset for training, while unsupervised learning seeks hidden patterns from unlabeled data without any feedback loop. Transductive learning, on the other hand, focuses on making predictions based on a small labeled subset in conjunction with a larger, unlabeled set, but it does not incorporate an ongoing interaction and adaptation process like reinforcement learning does