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State and Action Factorization in Power Grids
Gianvito Losapio, Davide Beretta, Marco Mussi +2
The increase of renewable energy generation towards the zero-emission target is making the problem of controlling power grids more and more challenging. The recent series of compet…
Bridging Rested and Restless Bandits with Graph-Triggering: Rising and Rotting
Gianmarco Genalti, Marco Mussi, Nicola Gatti +3
Rested and Restless Bandits are two well-known bandit settings that are useful to model real-world sequential decision-making problems in which the expected reward of an arm evolve…
Open Problem: Tight Bounds for Kernelized Multi-Armed Bandits with Bernoulli Rewards
Marco Mussi, Simone Drago, Alberto Maria Metelli
We consider Kernelized Bandits (KBs) to optimize a function belonging to the Reproducing Kernel Hilbert Space (RKHS) . Mainstream…
Last-Iterate Global Convergence of Policy Gradients for Constrained Reinforcement Learning
Alessandro Montenegro, Marco Mussi, Matteo Papini +1
Constrained Reinforcement Learning (CRL) tackles sequential decision-making problems where agents are required to achieve goals by maximizing the expected return while meeting doma…
Learning Optimal Deterministic Policies with Stochastic Policy Gradients
Alessandro Montenegro, Marco Mussi, Alberto Maria Metelli +1
Policy gradient (PG) methods are successful approaches to deal with continuous reinforcement learning (RL) problems. They learn stochastic parametric (hyper)policies by either expl…