Inverse Reinforcement Learning (IRL) -- the problem of learning reward functions from demonstrations of an \emph{expert policy} -- plays a critical role in developing intelligent systems, such as those that understand and imitate human behavior. While widely used in applications, theoretical understandings of IRL admit unique challenges and remain less developed compared with standard RL theory. For example, it remains open how to do IRL efficiently in standard \emph{offline} settings with pre-collected data, where states are obtained from a \emph{behavior policy} (which could be the expert policy itself), and actions are sampled from the expert policy. This paper provides the first line of results for efficient IRL in vanilla offline and online settings using polynomial samples and runtime. We first design a new IRL algorithm for the offline setting, Reward Learning with Pessimism (RLP), and show that it achieves polynomial sample complexity in terms of the size of the MDP, a concentrability coefficient between the behavior policy and the expert policy, and the desired accuracy. Building on RLP, we further design an algorithm Reward Learning with Exploration (RLE), which operates in a natural online setting where the learner can both actively explore the environment and query the expert policy, and obtain a stronger notion of IRL guarantee from polynomial samples. We establish sample complexity lower bounds for both settings showing that RLP and RLE are nearly optimal. Finally, as an application, we show that the learned reward functions can \emph{transfer} to another target MDP with suitable guarantees when the target MDP satisfies certain similarity assumptions with the original (source) MDP.

逆强化学习（IRL）是学习奖励函数的问题，该问题通过对专家策略的演示来开发理解和模仿人类行为等智能系统起着关键作用。本文提供了首批有效的IRL结果，包括离线和在线设置，使用多项式采样和运行时间等方面的效率。使用RLP和RLE算法，我们分别设计了离线和在线设置的IRL算法，并建立了样本复杂度的下界，表明RLP和RLE算法是近乎最优的。此外，我们还展示了学习的奖励函数在具有适当相似性假设的目标MDP上的转移能力。

反向强化学习是否比标准强化学习更困难？