8 citations · 15 across the 5 of their papers we have counts for
7 papers
Score vs. Winrate in Score-Based Games: which Reward for Reinforcement Learning?
Luca Pasqualini, Gianluca Amato, Marco Fantozzi +5
In the last years, the DeepMind algorithm AlphaZero has become the state of the art to efficiently tackle perfect information two-player zero-sum games with a win/lose outcome. How…
Messing Up 3D Virtual Environments: Transferable Adversarial 3D Objects
Enrico Meloni, Matteo Tiezzi, Luca Pasqualini +2
In the last few years, the scientific community showed a remarkable and increasing interest towards 3D Virtual Environments, training and testing Machine Learning-based models in r…
Deep Reinforcement Learning for URLLC data management on top of scheduled eMBB traffic
Fabio Saggese, Luca Pasqualini, Marco Moretti +1
With the advent of 5G and the research into beyond 5G (B5G) networks, a novel and very relevant research issue is how to manage the coexistence of different types of traffic, each…
Generate and Revise: Reinforcement Learning in Neural Poetry
Andrea Zugarini, Luca Pasqualini, Stefano Melacci +1
Writers, poets, singers usually do not create their compositions in just one breath. Text is revisited, adjusted, modified, rephrased, even multiple times, in order to better conve…
Pseudo Random Number Generation through Reinforcement Learning and Recurrent Neural Networks
Luca Pasqualini, Maurizio Parton
A Pseudo-Random Number Generator (PRNG) is any algorithm generating a sequence of numbers approximating properties of random numbers. These numbers are widely employed in mid-level…
SAILenv: Learning in Virtual Visual Environments Made Simple
Enrico Meloni, Luca Pasqualini, Matteo Tiezzi +2
Recently, researchers in Machine Learning algorithms, Computer Vision scientists, engineers and others, showed a growing interest in 3D simulators as a mean to artificially create…