2 papers
cs.MA2026
Heterogeneous RBCs via Deep Multi-Agent Reinforcement Learning
Federico Gabriele, Aldo Glielmo, Marco Taboga
Current macroeconomic models with agent heterogeneity can be broadly divided into two main groups. Heterogeneous-agent general equilibrium (GE) models, such as those based on Heter…
cs.LG2025
Natural-gas storage modelling by deep reinforcement learning
Tiziano Balaconi, Aldo Glielmo, Marco Taboga
We introduce GasRL, a simulator that couples a calibrated representation of the natural gas market with a model of storage-operator policies trained with deep reinforcement learnin…