Adversarial inverse reinforcement learning (AIRL)
Adversarial inverse reinforcement learning (AIRL) is a framework that combines adversarial learning principles with inverse reinforcement learning to derive rewards from expert behavior.
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Adversarial inverse reinforcement learning (AIRL) is a framework that combines adversarial learning principles with inverse reinforcement learning to derive rewards from expert behavior.
Apprenticeship learning is a method in machine learning where an agent learns to perform tasks by observing expert demonstrations. It is closely related to imitation learning and is used to teach autonomous systems by mimicking expert behavior rather than relying solely on trial-and-error.