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Found 8 papers, 6 papers with code
Bridging Distributionally Robust Learning and Offline RL: An Approach to Mitigate Distribution Shift and Partial Data Coverage
The goal of an offline reinforcement learning (RL) algorithm is to learn optimal polices using historical (offline) data, without access to the environment for online exploration.
Improved Sample Complexity Bounds for Distributionally Robust Reinforcement Learning
We formulate this as a distributionally robust reinforcement learning (DR-RL) problem where the objective is to learn the policy which maximizes the value function against the worst possible stochastic model of the environment in an uncertainty set.
Personalized Reward Learning with Interaction-Grounded Learning (IGL)
In an era of countless content offerings, recommender systems alleviate information overload by providing users with personalized content suggestions.
Robust Reinforcement Learning using Offline Data
The goal of robust reinforcement learning (RL) is to learn a policy that is robust against the uncertainty in model parameters.
Off-Policy Evaluation Using Information Borrowing and Context-Based Switching
We consider the off-policy evaluation (OPE) problem in contextual bandits, where the goal is to estimate the value of a target policy using the data collected by a logging policy.
Sample Complexity of Robust Reinforcement Learning with a Generative Model
For each of these uncertainty sets, we give a precise characterization of the sample complexity of our proposed algorithm.
Model-based Reinforcement Learning
reinforcement-learning
+1
Robust Reinforcement Learning using Least Squares Policy Iteration with Provable Performance Guarantees
We first propose the Robust Least Squares Policy Evaluation algorithm, which is a multi-step online model-free learning algorithm for policy evaluation.
Bounded Regret for Finitely Parameterized Multi-Armed Bandits
We propose an algorithm that is simple and easy to implement, which we call Finitely Parameterized Upper Confidence Bound (FP-UCB) algorithm, which uses the information about the underlying parameter set for faster learning.
