In this letter we consider a distributed stochastic optimization framework in which agents network aim to cooperatively learn an optimal network-wide policy. The goal is compute local functions minimize the expected value of given cost, subject individual constraints and average coupling constraints. order handle challenges context, resort Lagrangian duality approach that allows us derive assoc...

Molecule optimization is a fundamental task for accelerating drug discovery, with the goal of generating new valid molecules that maximize multiple properties while maintaining similarity to input molecule. Existing generative models and reinforcement learning approaches made initial success, but still face difficulties in simultaneously optimizing properties. To address such challenges, we pro...