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08/29/2023
Pure Exploration under Mediators' Feedback
Stochastic multi-armed bandits are a sequential-decision-making framewor...
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06/19/2023
Nonlinear Feature Aggregation: Two Algorithms driven by Theory
Many real-world machine learning applications are characterized by a hug...
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05/10/2023
An Option-Dependent Analysis of Regret Minimization Algorithms in Finite-Horizon Semi-Markov Decision Processes
A large variety of real-world Reinforcement Learning (RL) tasks is chara...
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05/07/2023
Truncating Trajectories in Monte Carlo Reinforcement Learning
In Reinforcement Learning (RL), an agent acts in an unknown environment ...
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04/25/2023
Towards Theoretical Understanding of Inverse Reinforcement Learning
Inverse reinforcement learning (IRL) denotes a powerful family of algori...
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04/11/2023
A Tale of Sampling and Estimation in Discounted Reinforcement Learning
The most relevant problems in discounted reinforcement learning involve ...
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03/26/2023
Interpretable Linear Dimensionality Reduction based on Bias-Variance Analysis
One of the central issues of several machine learning applications on re...
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03/14/2023
Information-Theoretic Regret Bounds for Bandits with Fixed Expert Advice
We investigate the problem of bandits with expert advice when the expert...
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03/04/2023
Wasserstein Actor-Critic: Directed Exploration via Optimism for Continuous-Actions Control
Uncertainty quantification has been extensively used as a means to achie...
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02/15/2023
Best Arm Identification for Stochastic Rising Bandits
Stochastic Rising Bandits is a setting in which the values of the expect...
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12/12/2022
Autoregressive Bandits
Autoregressive processes naturally arise in a large variety of real-worl...
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12/07/2022
Tight Performance Guarantees of Imitator Policies with Continuous Actions
Behavioral Cloning (BC) aims at learning a policy that mimics the behavi...
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12/07/2022
Stochastic Rising Bandits
This paper is in the field of stochastic Multi-Armed Bandits (MABs), i.e...
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11/21/2022
Simultaneously Updating All Persistence Values in Reinforcement Learning
In reinforcement learning, the performance of learning agents is highly ...
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11/16/2022
Dynamical Linear Bandits
In many real-world sequential decision-making problems, an action does n...
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07/25/2022
Optimizing Empty Container Repositioning and Fleet Deployment via Configurable Semi-POMDPs
With the continuous growth of the global economy and markets, resource i...
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07/08/2022
Storehouse: a Reinforcement Learning Environment for Optimizing Warehouse Management
Warehouse Management Systems have been evolving and improving thanks to ...
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05/20/2022
ARLO: A Framework for Automated Reinforcement Learning
Automated Reinforcement Learning (AutoRL) is a relatively new area of re...
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12/13/2021
Lifelong Hyper-Policy Optimization with Multiple Importance Sampling Regularization
Learning in a lifelong setting, where the dynamics continually evolve, i...
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12/15/2020
Policy Optimization as Online Learning with Mediator Feedback
Policy Optimization (PO) is a widely used approach to address continuous...
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02/17/2020
Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning
The choice of the control frequency of a system has a relevant impact on...
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09/09/2019
Gradient-Aware Model-based Policy Search
Traditional model-based reinforcement learning approaches learn a model ...
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09/09/2019
Policy Space Identification in Configurable Environments
We study the problem of identifying the policy space of a learning agent...
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07/17/2019
Feature Selection via Mutual Information: New Theoretical Insights
Mutual information has been successfully adopted in filter feature-selec...
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09/17/2018
Policy Optimization via Importance Sampling
Policy optimization is an effective reinforcement learning approach to s...
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06/14/2018