In coming up with solutions to real-world problems, humans implicitly adhere to constraints that are too numerous and complex to be specified completely. However, reinforcement learning (RL) agents need these constraints to learn the correct optimal policy in these settings. The field of Inverse Constraint Reinforcement Learning (ICRL) deals with this problem and provides algorithms that aim to estimate the constraints from expert demonstrations collected offline. Practitioners prefer to know a measure of confidence in the estimated constraints, before deciding to use these constraints, which allows them to only use the constraints that satisfy a desired level of confidence. However, prior works do not allow users to provide the desired level of confidence for the inferred constraints. This work provides a principled ICRL method that can take a confidence level with a set of expert demonstrations and outputs a constraint that is at least as constraining as the true underlying constraint with the desired level of confidence. Further, unlike previous methods, this method allows a user to know if the number of expert trajectories is insufficient to learn a constraint with a desired level of confidence, and therefore collect more expert trajectories as required to simultaneously learn constraints with the desired level of confidence and a policy that achieves the desired level of performance.

逆向约束强化学习 (Inverse Constraint Reinforcement Learning, ICRL) 领域研究了从离线专家演示中估计约束的算法，并提供了一种基于置信水平对专家演示进行约束估计的方法，使用户可以选择满足期望置信水平的约束进行使用，同时允许用户了解专家轨迹数量不足时的情况并采集更多专家轨迹以同时学习满足期望置信水平的约束和达到期望性能水平的策略。

自信感感知的逆约束强化学习