3 citations · 3 across the 3 of their papers we have counts for
3 papers
4Hammer: a board-game reinforcement learning environment for the hour long time frame
Massimo Fioravanti, Giovanni Agosta
Large Language Models (LLMs) have demonstrated strong performance on tasks with short time frames, but struggle with tasks requiring longer durations. While datasets covering exten…
Rulebook: bringing co-routines to reinforcement learning environments
Massimo Fioravanti, Samuele Pasini, Giovanni Agosta
Reinforcement learning (RL) algorithms, due to their reliance on external systems to learn from, require digital environments (e.g., simulators) with very simple interfaces, which…
Array-Aware Matching: Taming the Complexity of Large-Scale Simulation Models
Massimo Fioravanti, Daniele Cattaneo, Federico Terraneo +5
Equation-based modelling is a powerful approach to tame the complexity of large-scale simulation problems. Equation-based tools automatically translate models into imperative langu…