20 citations · 69 across the 50 of their papers we have counts for
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Online Bayesian Persuasion Without a Clue
Francesco Bacchiocchi, Matteo Bollini, Matteo Castiglioni +2
We study online Bayesian persuasion problems in which an informed sender repeatedly faces a receiver with the goal of influencing their behavior through the provision of payoff-rel…
Contracting With a Reinforcement Learning Agent by Playing Trick or Treat
Matteo Bollini, Francesco Bacchiocchi, Matteo Castiglioni +2
We study principal-agent problems where a farsighted agent takes costly actions in an MDP. The core challenge in these settings is that agent's actions are hidden to the principal,…
Optimal Strong Regret and Violation in Constrained MDPs via Policy Optimization
Francesco Emanuele Stradi, Matteo Castiglioni, Alberto Marchesi +1
We study online learning in \emph{constrained MDPs} (CMDPs), focusing on the goal of attaining sublinear strong regret and strong cumulative constraint violation. Differently from…
Best-of-Both-Worlds Policy Optimization for CMDPs with Bandit Feedback
Francesco Emanuele Stradi, Anna Lunghi, Matteo Castiglioni +2
We study online learning in constrained Markov decision processes (CMDPs) in which rewards and constraints may be either stochastic or adversarial. In such settings, Stradi et al.(…
The Sample Complexity of Stackelberg Games
Francesco Bacchiocchi, Matteo Bollini, Matteo Castiglioni +2
Stackelberg games (SGs) constitute the most fundamental and acclaimed models of strategic interactions involving some form of commitment. Moreover, they form the basis of more elab…
Learning Constrained Markov Decision Processes With Non-stationary Rewards and Constraints
Francesco Emanuele Stradi, Anna Lunghi, Matteo Castiglioni +2
In constrained Markov decision processes (CMDPs) with adversarial rewards and constraints, a well-known impossibility result prevents any algorithm from attaining both sublinear re…
Markov Persuasion Processes: Learning to Persuade from Scratch
Francesco Bacchiocchi, Francesco Emanuele Stradi, Matteo Castiglioni +2
In Bayesian persuasion, an informed sender strategically discloses information to a receiver so as to persuade them to undertake desirable actions. Recently, a growing attention ha…
Learning Adversarial MDPs with Stochastic Hard Constraints
Francesco Emanuele Stradi, Matteo Castiglioni, Alberto Marchesi +1
We study online learning in constrained Markov decision processes (CMDPs) with adversarial losses and stochastic hard constraints, under bandit feedback. We consider three scenario…
Regret-Minimizing Contracts: Agency Under Uncertainty
Martino Bernasconi, Matteo Castiglioni, Alberto Marchesi
We study the fundamental problem of designing contracts in principal-agent problems under uncertainty. Previous works mostly addressed Bayesian settings in which principal's uncert…