Offline inverse reinforcement learning (Offline IRL) aims to recover the structure of rewards and environment dynamics that underlie observed actions in a fixed, finite set of demonstrations from an expert agent. Accurate models of expertise in executing a task has applications in safety-sensitive applications such as clinical decision making and autonomous driving. However, the structure of an expert's preferences implicit in observed actions is closely linked to the expert's model of the environment dynamics (i.e. the ``world''). Thus, inaccurate models of the world obtained from finite data with limited coverage could compound inaccuracy in estimated rewards. To address this issue, we propose a bi-level optimization formulation of the estimation task wherein the upper level is likelihood maximization based upon a conservative model of the expert's policy (lower level). The policy model is conservative in that it maximizes reward subject to a penalty that is increasing in the uncertainty of the estimated model of the world. We propose a new algorithmic framework to solve the bi-level optimization problem formulation and provide statistical and computational guarantees of performance for the associated reward estimator. Finally, we demonstrate that the proposed algorithm outperforms the state-of-the-art offline IRL and imitation learning benchmarks by a large margin, over the continuous control tasks in MuJoCo and different datasets in the D4RL benchmark.

翻译：离线逆强化学习旨在从专家代理提供的固定有限示范集合中，恢复驱动观察动作的奖励与环境动态结构。对任务执行专业性的精确建模在临床决策和自动驾驶等安全敏感应用中具有重要意义。然而，观察动作中隐含的专家偏好结构与专家对环境动态（即"世界"）的建模密切相关。因此，基于有限覆盖范围的有限数据所得的不精确世界模型，可能加剧奖励估计的误差。为解决该问题，我们提出一种双层优化估计框架：上层基于专家策略（下层）的保守模型进行似然最大化，其中保守策略模型在最大化奖励的同时，对估计世界模型的不确定性施加递增惩罚。我们提出新的算法框架求解该双层优化问题，并为相关奖励估计器提供统计与计算性能保证。最后，实验表明，在MuJoCo连续控制任务及D4RL基准的不同数据集上，所提算法大幅优于现有最优离线IRL与模仿学习基准方法。