Inverse reinforcement learning explores how agents can learn behaviors by observing actions rather than relying on predefined rewards. By watching others, such as humans or other machines, the agent deduces the underlying reward function that motivates those actions. This approach is closely related to imitation learning, offering a fascinating perspective on how machines can adapt and learn from their environment.
