35 citations · 59 across the 31 of their papers we have counts for
46 papers · 1 filter
K-Myriad: Jump-starting reinforcement learning with unsupervised parallel agents
Vincenzo De Paola, Mirco Mutti, Riccardo Zamboni +1
Parallelization in Reinforcement Learning is typically employed to speed up the training of a single policy, where multiple workers collect experience from an identical sampling di…
Online Dynamic Pricing of Complementary Products
Marco Mussi, Marcello Restelli
Traditional pricing paradigms, once dominated by static models and rule-based heuristics, are increasingly being replaced by dynamic, data-driven approaches powered by machine lear…
Power Grid Control with Graph-Based Distributed Reinforcement Learning
Carlo Fabrizio, Gianvito Losapio, Marco Mussi +2
The necessary integration of renewable energy sources, combined with the expanding scale of power networks, presents significant challenges in controlling modern power grids. Tradi…
From Parameters to Behaviors: Unsupervised Compression of the Policy Space
Davide Tenedini, Riccardo Zamboni, Mirco Mutti +1
Despite its recent successes, Deep Reinforcement Learning (DRL) is notoriously sample-inefficient. We argue that this inefficiency stems from the standard practice of optimizing po…
Building surrogate models using trajectories of agents trained by Reinforcement Learning
Julen Cestero, Marco Quartulli, Marcello Restelli
Sample efficiency in the face of computationally expensive simulations is a common concern in surrogate modeling. Current strategies to minimize the number of samples needed are no…
Limitations of Physics-Informed Neural Networks: a Study on Smart Grid Surrogation
Julen Cestero, Carmine Delle Femine, Kenji S. Muro +2
Physics-Informed Neural Networks (PINNs) present a transformative approach for smart grid modeling by integrating physical laws directly into learning frameworks, addressing critic…