arXiv stat.ML
· Papers
Learning from the Unseen: Offline Reinforcement Learning with Hidden Actions
arXiv:2607.25241v1 Announce Type: new Abstract: Standard offline reinforcement learning (RL) algorithms typically assume that the actions in the dataset are observed without error. However, in many real-world applications, the true actions are unobserved and only noisy proxies are available, causing existing RL methods