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Inference on the Value of Policy Improvement in Adaptive Experiments
Justin Whitehouse, Emma Brunskill, Vasilis Syrgkanis
In this paper, we develop an asymptotically normal estimator for the value of policy improvement (VPI) under adaptively collected data. This value, which is defined as the gap betw…
Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding
Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky +1
When observed decisions depend only on observed features, off-policy policy evaluation (OPE) methods for sequential decision making problems can estimate the performance of evaluat…
Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning
Christoph Dann, Emma Brunskill
Recently, there has been significant progress in understanding reinforcement learning in discounted infinite-horizon Markov decision processes (MDPs) by deriving tight sample compl…
Online Stochastic Optimization under Correlated Bandit Feedback
Mohammad Gheshlaghi Azar, Alessandro Lazaric, Emma Brunskill
In this paper we consider the problem of online stochastic optimization of a locally smooth function under bandit feedback. We introduce the high-confidence tree (HCT) algorithm, a…
Sequential Transfer in Multi-armed Bandit with Finite Set of Models
Mohammad Gheshlaghi Azar, Alessandro Lazaric, Emma Brunskill
Learning from prior tasks and transferring that experience to improve future performance is critical for building lifelong learning agents. Although results in supervised and reinf…
Regret Bounds for Reinforcement Learning with Policy Advice
Mohammad Gheshlaghi Azar, Alessandro Lazaric, Emma Brunskill
In some reinforcement learning problems an agent may be provided with a set of input policies, perhaps learned from prior experience or provided by advisors. We present a reinforce…