20 citations · 52 across the 9 of their papers we have counts for
10 papers
Trajectory Data Suffices for Statistically Efficient Learning in Offline RL with Linear -Realizability and Concentrability
Volodymyr Tkachuk, Gellért Weisz, Csaba Szepesvári
We consider offline reinforcement learning (RL) in -horizon Markov decision processes (MDPs) under the linear -realizability assumption, where the action-value function of…
Online RL in Linearly -Realizable MDPs Is as Easy as in Linear MDPs If You Learn What to Ignore
Gellért Weisz, András György, Csaba Szepesvári
We consider online reinforcement learning (RL) in episodic Markov decision processes (MDPs) under the linear -realizability assumption, where it is assumed that the action-val…
Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL
Qinghua Liu, Gellért Weisz, András György +2
While policy optimization algorithms have played an important role in recent empirical success of Reinforcement Learning (RL), the existing theoretical understanding of policy opti…
Exponential Hardness of Reinforcement Learning with Linear Function Approximation
Daniel Kane, Sihan Liu, Shachar Lovett +3
A fundamental question in reinforcement learning theory is: suppose the optimal value functions are linear in given features, can we learn them efficiently? This problem's counterp…
Confident Approximate Policy Iteration for Efficient Local Planning in -realizable MDPs
Gellért Weisz, András György, Tadashi Kozuno +1
We consider approximate dynamic programming in -discounted Markov decision processes and apply it to approximate planning with linear value-function approximation. Our first con…
On Query-efficient Planning in MDPs under Linear Realizability of the Optimal State-value Function
Gellért Weisz, Philip Amortila, Barnabás Janzer +3
We consider local planning in fixed-horizon MDPs with a generative model under the assumption that the optimal value function lies close to the span of a feature map. The generativ…
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…
Exploration-Enhanced POLITEX
Yasin Abbasi-Yadkori, Nevena Lazic, Csaba Szepesvari +1
We study algorithms for average-cost reinforcement learning problems with value function approximation. Our starting point is the recently proposed POLITEX algorithm, a version of…
LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration
Gellért Weisz, András György, Csaba Szepesvári
We consider the problem of configuring general-purpose solvers to run efficiently on problem instances drawn from an unknown distribution. The goal of the configurator is to find a…
Sample Efficient Deep Reinforcement Learning for Dialogue Systems with Large Action Spaces
Gellért Weisz, Paweł Budzianowski, Pei-Hao Su +1
In spoken dialogue systems, we aim to deploy artificial intelligence to build automated dialogue agents that can converse with humans. A part of this effort is the policy optimisat…