想学逆强化学习？QVIRL能边学边给不确定性，还能主动问你要数据，比老方法稳多了。
QVIRL是一种新型贝叶斯逆强化学习方法，通过变分分布学习最优Q值，从专家演示中推断奖励函数后验。该方法结合了可扩展性与不确定性量化，适用于安全关键应用和主动学习。在网格世界、Lunar Lander、Highway Environment及两个ATARI游戏等任务中表现优异，且是首个能从原始像素观测训练的贝叶斯IRL方法。
Q-based Variational Inverse Reinforcement Learning
The development of safe and beneficial AI requires that systems can learn and act in accordance with human preferences. However, explicitly specifying these preferences by hand is often infeasible. Inverse reinforcement learning (IRL) addresses this challenge by inferring preferences, represented as reward functions, from expert behaviour. We introduce Q-based Variational IRL (QVIRL), a novel Bayesian IRL method that recovers a posterior distribution over rewards from expert demonstrations via primarily learning a variational distribution over optimal Q-values. Unlike previous approaches, QVIRL combines scalability with uncertainty quantification, important for safety-critical applications as well as active learning. We demonstrate QVIRL's strong performance in apprenticeship learning across various tasks, including gridworlds, Lunar Lander, the Highway Environment, and two ATARI games both with static expert data and with active learning. It is the first method for Bayesian IRL that demonstrates training from raw pixel observations.