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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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06/13/2023
Stepsize Learning for Policy Gradient Methods in Contextual Markov Decision Processes
Policy-based algorithms are among the most widely adopted techniques in ...
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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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11/17/2022
Dynamic Pricing with Volume Discounts in Online Settings
According to the main international reports, more pervasive industrial a...
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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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06/03/2022
Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review
Atmospheric Extreme Events (EEs) cause severe damages to human societies...
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06/01/2022
Multi-Armed Bandit Problem with Temporally-Partitioned Rewards: When Partial Feedback Counts
There is a rising interest in industrial online applications where data ...
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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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05/11/2022
Delayed Reinforcement Learning by Imitation
When the agent's observations or interactions are delayed, classic reinf...
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02/22/2022
Reward-Free Policy Space Compression for Reinforcement Learning
In reinforcement learning, we encode the potential behaviors of an agent...
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02/14/2022
Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization
In the sequential decision making setting, an agent aims to achieve syst...
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02/07/2022
The Importance of Non-Markovianity in Maximum State Entropy Exploration
In the maximum state entropy exploration framework, an agent interacts w...
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02/03/2022
Challenging Common Assumptions in Convex Reinforcement Learning
The classic Reinforcement Learning (RL) formulation concerns the maximiz...
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12/16/2021
Unsupervised Reinforcement Learning in Multiple Environments
Several recent works have been dedicated to unsupervised reinforcement l...
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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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10/27/2021
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection
We study the role of the representation of state-action value functions ...
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05/18/2021
Meta-Reinforcement Learning by Tracking Task Non-stationarity
Many real-world domains are subject to a structured non-stationarity whi...
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04/08/2021
Leveraging Good Representations in Linear Contextual Bandits
The linear contextual bandit literature is mostly focused on the design ...
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03/17/2021
A Practical Guide to Multi-Objective Reinforcement Learning and Planning
Real-world decision-making tasks are generally complex, requiring trade-...
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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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10/23/2020
An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits
In the contextual linear bandit setting, algorithms built on the optimis...
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10/23/2020
Option Hedging with Risk Averse Reinforcement Learning
In this paper we show how risk-averse reinforcement learning can be used...
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07/15/2020
Inverse Reinforcement Learning from a Gradient-based Learner
Inverse Reinforcement Learning addresses the problem of inferring an exp...
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07/15/2020
Newton-based Policy Optimization for Games
Many learning problems involve multiple agents optimizing different inte...
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07/09/2020
A Policy Gradient Method for Task-Agnostic Exploration
In a reward-free environment, what is a suitable intrinsic objective for...
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07/01/2020
Sequential Transfer in Reinforcement Learning with a Generative Model
We are interested in how to design reinforcement learning agents that pr...
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05/26/2020
Time-Variant Variational Transfer for Value Functions
In most transfer learning approaches to reinforcement learning (RL) the ...
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05/23/2020
A Novel Confidence-Based Algorithm for Structured Bandits
We study finite-armed stochastic bandits where the rewards of each arm m...
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03/03/2020
Online Joint Bid/Daily Budget Optimization of Internet Advertising Campaigns
Pay-per-click advertising includes various formats (e.g., search, contex...
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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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01/04/2020
MushroomRL: Simplifying Reinforcement Learning Research
MushroomRL is an open-source Python library developed to simplify the pr...
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12/06/2019
Risk-Averse Trust Region Optimization for Reward-Volatility Reduction
In real-world decision-making problems, for instance in the fields of fi...
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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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07/10/2019