255 citations · 2.4k across the 127 of their papers we have counts for
15 papers · 2 filters
Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient
Botao Hao, Yaqi Duan, Tor Lattimore +2
This paper provides a statistical analysis of high-dimensional batch Reinforcement Learning (RL) using sparse linear function approximation. When there is a large number of candida…
Online Sparse Reinforcement Learning
Botao Hao, Tor Lattimore, Csaba Szepesvári +1
We investigate the hardness of online reinforcement learning in fixed horizon, sparse linear Markov decision process (MDP), with a special focus on the high-dimensional regime wher…
Online Algorithm for Unsupervised Sequential Selection with Contextual Information
Arun Verma, Manjesh K. Hanawal, Csaba Szepesvári +1
In this paper, we study Contextual Unsupervised Sequential Selection (USS), a new variant of the stochastic contextual bandits problem where the loss of an arm cannot be inferred f…
CoinDICE: Off-Policy Confidence Interval Estimation
Bo Dai, Ofir Nachum, Yinlam Chow +3
We study high-confidence behavior-agnostic off-policy evaluation in reinforcement learning, where the goal is to estimate a confidence interval on a target policy's value, given on…
Exponential Lower Bounds for Planning in MDPs With Linearly-Realizable Optimal Action-Value Functions
Gellért Weisz, Philip Amortila, Csaba Szepesvári
We consider the problem of local planning in fixed-horizon and discounted Markov Decision Processes (MDPs) with linear function approximation and a generative model under the assum…
Efficient Planning in Large MDPs with Weak Linear Function Approximation
Roshan Shariff, Csaba Szepesvári
Large-scale Markov decision processes (MDPs) require planning algorithms with runtime independent of the number of states of the MDP. We consider the planning problem in MDPs using…