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Found 5 papers, 1 papers with code
Leveraging Offline Data in Linear Latent Bandits
One can leverage offline latent bandit data to learn a complex model for each latent state, so that an agent can simply learn the latent state online to act optimally.
A Theoretical Framework for Partially Observed Reward-States in RLHF
Both of these can be instrumental in speeding up learning and improving alignment.
Double Descent and Overfitting under Noisy Inputs and Distribution Shift for Linear Denoisers
Motivated by this, we study supervised denoising and noisy-input regression under distribution shift.
Offline Policy Evaluation and Optimization under Confounding
Evaluating and optimizing policies in the presence of unobserved confounders is a problem of growing interest in offline reinforcement learning.
Learning Mixtures of Markov Chains and MDPs
We present an algorithm for learning mixtures of Markov chains and Markov decision processes (MDPs) from short unlabeled trajectories.
