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How does Inverse Reinforcement Learning calculate an agent&#x27;s objective function compared to standard reinforcement learning?

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In standard reinforcement learning, the agent is provided with an explicit reward function, which is a mathematical formula that tells the agent the value of every state or action it takes. The agent’s objective is to learn a policy, or a strategy for behavior, that maximizes the cumulative sum of these rewards over time. In contrast, inverse reinforcement learning calculates an objective function when it is not provided. Instead, the process starts with expert demonstrations, which are sets of data showing how an ideal agent behaves in a given environment. Inverse reinforcement learning treats the expert’s behavior as optimal and works backward to derive the underlying reward function that would make those specific demonstrations appear as the best possible actions. This means the agent first calculates the hidden intent or values of the expert by observing their performance, and only then uses standard reinforcement learning techniques to find a policy that achieves those same inferred goals. Essentially, standard reinforcement learning goes from a reward function to behavior, while inverse reinforcement learning goes from behavior to a reward function.

How does Inverse Reinforcement Learning calculate an agent's objective function compared to standard reinforcement learning?

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How does Inverse Reinforcement Learning calculate an agent's objective function compared to standard reinforcement learning?

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