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cs.LG2022
Offline Policy Optimization with Eligible Actions
Yao Liu, Yannis Flet-Berliac, Emma Brunskill
Offline policy optimization could have a large impact on many real-world decision-making problems, as online learning may be infeasible in many applications. Importance sampling an…
cs.LG2022
Provably Sample-Efficient RL with Side Information about Latent Dynamics
Yao Liu, Dipendra Misra, Miro Dudík +1
We study reinforcement learning (RL) in settings where observations are high-dimensional, but where an RL agent has access to abstract knowledge about the structure of the state sp…