Markov Models
Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback
In this work, we study the low-rank MDPs with adversarially changed losses in the full-information feedback setting.
Achieving O (1 /ε) Sample Complexity for Constrained Markov Decision Process
We consider the reinforcement learning problem for the constrained Markov decision process (CMDP), which plays a central role in satisfying safety or resource constraints in sequential learning and decision-making.
Two-way Deconfounder for Off-policy Evaluation in Causal Reinforcement Learning Shuguang Y u
Before deploying any newly developed policy, it is important to assess its impact. In many high-stakes domains, it is risky or unethical to implement such policies directly for online evaluation.
One Demonstration is Enough to Learn Robot Policies
Sequential decision-making problems typically require significant human supervision and data.
A Supplementary Material This Supplementary Material section provides additional details and discussions to complement our
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