2 papers
cs.LG2026
Complexity-Regularized Proximal Policy Optimization
Luca Serfilippi, Giorgio Franceschelli, Antonio Corradi +1
Policy gradient methods usually rely on entropy regularization to prevent premature convergence. However, maximizing entropy indiscriminately pushes the policy towards a uniform di…
physics.chem-ph2025
Quantum Chemistry Driven Molecular Inverse Design with Data-free Reinforcement Learning
Francesco Calcagno, Luca Serfilippi, Giorgio Franceschelli +3
The inverse design of molecules has challenged chemists for decades. In the past years, machine learning and artificial intelligence have emerged as new tools to generate molecules…