
Improved Sample Complexity for Incremental Autonomous Exploration in MDPs
We investigate the exploration of an unknown environment when no reward ...
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An Asymptotically Optimal PrimalDual Incremental Algorithm for Contextual Linear Bandits
In the contextual linear bandit setting, algorithms built on the optimis...
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Local Differentially Private Regret Minimization in Reinforcement Learning
Reinforcement learning algorithms are widely used in domains where it is...
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A Provably Efficient Sample Collection Strategy for Reinforcement Learning
A common assumption in reinforcement learning (RL) is to have access to ...
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Improved Analysis of UCRL2 with Empirical Bernstein Inequality
We consider the problem of explorationexploitation in communicating Mar...
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A KernelBased Approach to NonStationary Reinforcement Learning in Metric Spaces
In this work, we propose KeRNS: an algorithm for episodic reinforcement ...
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Learning Adaptive Exploration Strategies in Dynamic Environments Through Informed Policy Regularization
We study the problem of learning explorationexploitation strategies tha...
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Regret Bounds for KernelBased Reinforcement Learning
We consider the explorationexploitation dilemma in finitehorizon reinf...
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Active Model Estimation in Markov Decision Processes
We study the problem of efficient exploration in order to learn an accur...
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ExplorationExploitation in Constrained MDPs
In many sequential decisionmaking problems, the goal is to optimize a u...
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Adversarial Attacks on Linear Contextual Bandits
Contextual bandit algorithms are applied in a wide range of domains, fro...
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Improved Algorithms for Conservative Exploration in Bandits
In many fields such as digital marketing, healthcare, finance, and robot...
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Conservative Exploration in Reinforcement Learning
While learning in an unknown Markov Decision Process (MDP), an agent sho...
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Concentration Inequalities for Multinoulli Random Variables
We investigate concentration inequalities for Dirichlet and Multinomial ...
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Exploiting Language Instructions for Interpretable and Compositional Reinforcement Learning
In this work, we present an alternative approach to making an agent comp...
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NoRegret Exploration in GoalOriented Reinforcement Learning
Many popular reinforcement learning problems (e.g., navigation in a maze...
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Frequentist Regret Bounds for Randomized LeastSquares Value Iteration
We consider the explorationexploitation dilemma in finitehorizon reinf...
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Smoothing Policies and Safe Policy Gradients
Policy gradient algorithms are among the best candidates for the much an...
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Exploration Bonus for Regret Minimization in Undiscounted Discrete and Continuous Markov Decision Processes
We introduce and analyse two algorithms for explorationexploitation in ...
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Near Optimal ExplorationExploitation in NonCommunicating Markov Decision Processes
While designing the state space of an MDP, it is common to include state...
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Stochastic VarianceReduced Policy Gradient
In this paper, we propose a novel reinforcement learning algorithm cons...
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Importance Weighted Transfer of Samples in Reinforcement Learning
We consider the transfer of experience samples (i.e., tuples < s, a, s',...
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Efficient BiasSpanConstrained ExplorationExploitation in Reinforcement Learning
We introduce SCAL, an algorithm designed to perform efficient exploratio...
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CostSensitive Approach to Batch Size Adaptation for Gradient Descent
In this paper, we propose a novel approach to automatically determine th...
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Multiobjective Reinforcement Learning with Continuous Pareto Frontier Approximation Supplementary Material
This document contains supplementary material for the paper "Multiobjec...
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Matteo Pirotta
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