Inverse Reinforcement Learning (IRL) is a powerful paradigm for inferring a reward function from expert demonstrations. Many IRL algorithms require a known transition model and sometimes even a known expert policy, or they at least require access to a generative model. However, these assumptions are too strong for many real-world applications, where the environment can be accessed only through sequential interaction. We propose a novel IRL algorithm: Active exploration for Inverse Reinforcement Learning (AceIRL), which actively explores an unknown environment and expert policy to quickly learn the expert's reward function and identify a good policy. AceIRL uses previous observations to construct confidence intervals that capture plausible reward functions and find exploration policies that focus on the most informative regions of the environment. AceIRL is the first approach to active IRL with sample-complexity bounds that does not require a generative model of the environment. AceIRL matches the sample complexity of active IRL with a generative model in the worst case. Additionally, we establish a problem-dependent bound that relates the sample complexity of AceIRL to the suboptimality gap of a given IRL problem. We empirically evaluate AceIRL in simulations and find that it significantly outperforms more naive exploration strategies.

翻译：逆强化学习（IRL）是一种从专家演示中推断奖励函数的强大范式。许多IRL算法需要已知的状态转移模型，有时甚至需要已知的专家策略，或者至少需要访问生成模型。然而，对于许多现实应用而言，这些假设过于严格，因为环境只能通过顺序交互访问。我们提出了一种新颖的IRL算法：面向逆强化学习的主动探索（AceIRL），该算法主动探索未知环境和专家策略，以快速学习专家的奖励函数并识别出优良策略。AceIRL利用先前的观测构建置信区间，以捕获可能的奖励函数，并寻找聚焦于环境中最具信息性区域的探索策略。AceIRL是首个无需环境生成模型、且具有样本复杂度边界的主动IRL方法。在最坏情况下，AceIRL的样本复杂度与使用生成模型的主动IRL相当。此外，我们建立了一个问题依赖的边界，将AceIRL的样本复杂度与给定IRL问题的次优性差距联系起来。我们通过仿真实验对AceIRL进行了实证评估，发现其显著优于更朴素的探索策略。