6 papers · 1 filter
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.(…
A Primal-Dual Online Learning Approach for Dynamic Pricing of Sequentially Displayed Complementary Items under Sale Constraints
Francesco Emanuele Stradi, Filippo Cipriani, Lorenzo Ciampiconi +3
We address the challenging problem of dynamically pricing complementary items that are sequentially displayed to customers. An illustrative example is the online sale of flight tic…
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…