Learning agents in AI adapt and improve over time by learning from their environment, making them essential for industries requiring flexibility and precision.
These agents do not rely on fixed programming; they evolve by gathering knowledge, executing tasks, and receiving feedback to optimize their actions.
The core components of learning agents include a learning element that collects information, a performance element for task execution, a critic for evaluation, and a problem generator.
Learning methods like supervised learning, unsupervised learning, and reinforcement learning empower these agents to recognize patterns, identify structures, and learn through trial and error.
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