A review of off-policy evaluation in reinforcement learning
Reinforcement learning (RL) is one of the most vibrant research frontiers in machine
learning and has been recently applied to solve a number of challenging problems. In this …
learning and has been recently applied to solve a number of challenging problems. In this …
[HTML][HTML] Batch policy learning in average reward markov decision processes
We consider the batch (off-line) policy learning problem in the infinite horizon Markov
Decision Process. Motivated by mobile health applications, we focus on learning a policy …
Decision Process. Motivated by mobile health applications, we focus on learning a policy …
Finite sample analysis of minimax offline reinforcement learning: Completeness, fast rates and first-order efficiency
We offer a theoretical characterization of off-policy evaluation (OPE) in reinforcement
learning using function approximation for marginal importance weights and $ q $-functions …
learning using function approximation for marginal importance weights and $ q $-functions …
On well-posedness and minimax optimal rates of nonparametric q-function estimation in off-policy evaluation
X Chen, Z Qi - International Conference on Machine …, 2022 - proceedings.mlr.press
We study the off-policy evaluation (OPE) problem in an infinite-horizon Markov decision
process with continuous states and actions. We recast the $ Q $-function estimation into a …
process with continuous states and actions. We recast the $ Q $-function estimation into a …
Off-policy confidence interval estimation with confounded markov decision process
This article is concerned with constructing a confidence interval for a target policy's value
offline based on a pre-collected observational data in infinite horizon settings. Most of the …
offline based on a pre-collected observational data in infinite horizon settings. Most of the …
An instrumental variable approach to confounded off-policy evaluation
Off-policy evaluation (OPE) aims to estimate the return of a target policy using some pre-
collected observational data generated by a potentially different behavior policy. In many …
collected observational data generated by a potentially different behavior policy. In many …
Future-dependent value-based off-policy evaluation in pomdps
We study off-policy evaluation (OPE) for partially observable MDPs (POMDPs) with general
function approximation. Existing methods such as sequential importance sampling …
function approximation. Existing methods such as sequential importance sampling …
Deeply-debiased off-policy interval estimation
Off-policy evaluation learns a target policy's value with a historical dataset generated by a
different behavior policy. In addition to a point estimate, many applications would benefit …
different behavior policy. In addition to a point estimate, many applications would benefit …
Online bootstrap inference for policy evaluation in reinforcement learning
The recent emergence of reinforcement learning (RL) has created a demand for robust
statistical inference methods for the parameter estimates computed using these algorithms …
statistical inference methods for the parameter estimates computed using these algorithms …
Bootstrapping fitted q-evaluation for off-policy inference
Bootstrapping provides a flexible and effective approach for assessing the quality of batch
reinforcement learning, yet its theoretical properties are poorly understood. In this paper, we …
reinforcement learning, yet its theoretical properties are poorly understood. In this paper, we …