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Found 25 papers, 3 papers with code
Distributional Off-policy Evaluation with Bellman Residual Minimization
We consider the problem of distributional off-policy evaluation which serves as the foundation of many distributional reinforcement learning (DRL) algorithms.
Robust Offline Reinforcement learning with Heavy-Tailed Rewards
This paper endeavors to augment the robustness of offline reinforcement learning (RL) in scenarios laden with heavy-tailed rewards, a prevalent circumstance in real-world applications.
Off-policy Evaluation in Doubly Inhomogeneous Environments
This work aims to study off-policy evaluation (OPE) under scenarios where two key reinforcement learning (RL) assumptions -- temporal stationarity and individual homogeneity are both violated.
A Policy Gradient Method for Confounded POMDPs
To the best of our knowledge, this is the first work studying the policy gradient method for POMDPs under the offline setting.
Sequential Knockoffs for Variable Selection in Reinforcement Learning
In real-world applications of reinforcement learning, it is often challenging to obtain a state representation that is parsimonious and satisfies the Markov property without prior knowledge.
Personalized Pricing with Invalid Instrumental Variables: Identification, Estimation, and Policy Learning
Specifically, relying on the structural models of revenue and price, we establish the identifiability condition of an optimal pricing strategy under endogeneity with the help of invalid instrumental variables.
PASTA: Pessimistic Assortment Optimization
The objective is to use the offline dataset to find an optimal assortment.
STEEL: Singularity-aware Reinforcement Learning
Batch reinforcement learning (RL) aims at leveraging pre-collected data to find an optimal policy that maximizes the expected total rewards in a dynamic environment.
Value Enhancement of Reinforcement Learning via Efficient and Robust Trust Region Optimization
When the initial policy is consistent, under some mild conditions, our method will yield a policy whose value converges to the optimal one at a faster rate than the initial policy, achieving the desired ``value enhancement" property.
Offline Reinforcement Learning for Human-Guided Human-Machine Interaction with Private Information
To tackle the distributional mismatch, we leverage the idea of pessimism and use our OPE method to develop an off-policy learning algorithm for finding a desirable policy pair for both Alice and Bob.
RISE: Robust Individualized Decision Learning with Sensitive Variables
This paper introduces RISE, a robust individualized decision learning framework with sensitive variables, where sensitive variables are collectible data and important to the intervention decision, but their inclusion in decision making is prohibited due to reasons such as delayed availability or fairness concerns.
Optimizing Pessimism in Dynamic Treatment Regimes: A Bayesian Learning Approach
In this article, we propose a novel pessimism-based Bayesian learning method for optimal dynamic treatment regimes in the offline setting.
Blessing from Human-AI Interaction: Super Reinforcement Learning in Confounded Environments
This approach utilizes the observed action, either from AI or humans, as input for achieving a stronger oracle in policy learning for the decision maker (humans or AI).
Off-Policy Evaluation for Episodic Partially Observable Markov Decision Processes under Non-Parametric Models
We study the problem of off-policy evaluation (OPE) for episodic Partially Observable Markov Decision Processes (POMDPs) with continuous states.
Offline Reinforcement Learning with Instrumental Variables in Confounded Markov Decision Processes
Due to the lack of online interaction with the environment, offline RL is facing the following two significant challenges: (i) the agent may be confounded by the unobserved state variables; (ii) the offline data collected a prior does not provide sufficient coverage for the environment.
On Well-posedness and Minimax Optimal Rates of Nonparametric Q-function Estimation in Off-policy Evaluation
We study the off-policy evaluation (OPE) problem in an infinite-horizon Markov decision process with continuous states and actions.
Pessimistic Model Selection for Offline Deep Reinforcement Learning
Deep Reinforcement Learning (DRL) has demonstrated great potentials in solving sequential decision making problems in many applications.
Rejoinder: Learning Optimal Distributionally Robust Individualized Treatment Rules
However, when the growth of testing sample size available for training is in a slower order, efficient value function estimates may not perform well anymore.
Projected State-action Balancing Weights for Offline Reinforcement Learning
Offline policy evaluation (OPE) is considered a fundamental and challenging problem in reinforcement learning (RL).
Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confounding
Data-driven individualized decision making has recently received increasing research interests.
Robust Batch Policy Learning in Markov Decision Processes
We study the offline data-driven sequential decision making problem in the framework of Markov decision process (MDP).
Batch Policy Learning in Average Reward Markov Decision Processes
The performance of the method is illustrated by simulation studies and an analysis of a mobile health study promoting physical activity.
Learning Optimal Distributionally Robust Individualized Treatment Rules
We propose a novel distributionally robust ITR (DR-ITR) framework that maximizes the worst-case value function across the values under a set of underlying distributions that are "close" to the training distribution.
Statistical Analysis of Stationary Solutions of Coupled Nonconvex Nonsmooth Empirical Risk Minimization
This paper has two main goals: (a) establish several statistical properties---consistency, asymptotic distributions, and convergence rates---of stationary solutions and values of a class of coupled nonconvex and nonsmoothempirical risk minimization problems, and (b) validate these properties by a noisy amplitude-based phase retrieval problem, the latter being of much topical interest. Derived from available data via sampling, these empirical risk minimization problems are the computational workhorse of a population risk model which involves the minimization of an expected value of a random functional.
Estimation of Individualized Decision Rules Based on an Optimized Covariate-Dependent Equivalent of Random Outcomes
Recent exploration of optimal individualized decision rules (IDRs) for patients in precision medicine has attracted a lot of attention due to the heterogeneous responses of patients to different treatments.
