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Offline Inverse RL: New Solution Concepts and Provably Efficient Algorithms
In this paper, we introduce a novel notion of feasible reward set capturing the opportunities and limitations of the offline setting and we analyze the complexity of its estimation.
Towards Theoretical Understanding of Inverse Reinforcement Learning
We start by formally introducing the problem of estimating the feasible reward set, the corresponding PAC requirement, and discussing the properties of particular classes of rewards.
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