3 citations · 8 across the 13 of their papers we have counts for
14 papers
Theoretical Foundations of @ Reinforcement Learning
Riccardo Poiani, Martino Bernasconi, Andrea Celli
Reinforcement Learning is a cornerstone technique for modern large reasoning models. Usually, for difficult tasks such as code generation and theorem proving, the agent is evaluate…
Regret Minimization in Single-Dimensional Contract-Design with Binary Actions
Riccardo Poiani, Martino Bernasconi, Andrea Celli
We study principal-agent problems in which a principal commits to an outcome-dependent payment scheme (i.e., a contract) in order to induce an agent to take a costly action leading…
Optimal Rates for Feasible Payoff Set Estimation in Games
Annalisa Barbara, Riccardo Poiani, Martino Bernasconi +1
We study a setting in which two players play a (possibly approximate) Nash equilibrium of a bimatrix game, while a learner observes only their actions and has no knowledge of the e…
Non-Asymptotic Analysis of (Sticky) Track-and-Stop
Riccardo Poiani, Martino Bernasconi, Andrea Celli
In pure exploration problems, a statistician sequentially collects information to answer a question about some stochastic and unknown environment. The probability of returning a wr…
Pure Exploration with Infinite Answers
Riccardo Poiani, Martino Bernasconi, Andrea Celli
We study pure exploration problems in which the set of correct answers is possibly infinite. For example, such problems arise when regressing a continuous function on the means of…
Best-Arm Identification in Unimodal Bandits
Riccardo Poiani, Marc Jourdan, Emilie Kaufmann +1
We study the fixed-confidence best-arm identification problem in unimodal bandits, in which the means of the arms increase with the index of the arm up to their maximum, then decre…
Truncating Trajectories in Monte Carlo Policy Evaluation: an Adaptive Approach
Riccardo Poiani, Nicole Nobili, Alberto Maria Metelli +1
Policy evaluation via Monte Carlo (MC) simulation is at the core of many MC Reinforcement Learning (RL) algorithms (e.g., policy gradient methods). In this context, the designer of…
Optimal Multi-Fidelity Best-Arm Identification
Riccardo Poiani, Rémy Degenne, Emilie Kaufmann +2
In bandit best-arm identification, an algorithm is tasked with finding the arm with highest mean reward with a specified accuracy as fast as possible. We study multi-fidelity best-…
Inverse Reinforcement Learning with Sub-optimal Experts
Riccardo Poiani, Gabriele Curti, Alberto Maria Metelli +1
Inverse Reinforcement Learning (IRL) techniques deal with the problem of deducing a reward function that explains the behavior of an expert agent who is assumed to act optimally in…
Pure Exploration under Mediators' Feedback
Riccardo Poiani, Alberto Maria Metelli, Marcello Restelli
Stochastic multi-armed bandits are a sequential-decision-making framework, where, at each interaction step, the learner selects an arm and observes a stochastic reward. Within the…
Truncating Trajectories in Monte Carlo Reinforcement Learning
Riccardo Poiani, Alberto Maria Metelli, Marcello Restelli
In Reinforcement Learning (RL), an agent acts in an unknown environment to maximize the expected cumulative discounted sum of an external reward signal, i.e., the expected return.…
Optimizing Empty Container Repositioning and Fleet Deployment via Configurable Semi-POMDPs
Riccardo Poiani, Ciprian Stirbu, Alberto Maria Metelli +1
With the continuous growth of the global economy and markets, resource imbalance has risen to be one of the central issues in real logistic scenarios. In marine transportation, thi…
Meta-Reinforcement Learning by Tracking Task Non-stationarity
Riccardo Poiani, Andrea Tirinzoni, Marcello Restelli
Many real-world domains are subject to a structured non-stationarity which affects the agent's goals and the environmental dynamics. Meta-reinforcement learning (RL) has been shown…
Sequential Transfer in Reinforcement Learning with a Generative Model
Andrea Tirinzoni, Riccardo Poiani, Marcello Restelli
We are interested in how to design reinforcement learning agents that provably reduce the sample complexity for learning new tasks by transferring knowledge from previously-solved…