3 accepted papers
This work introduces and studies the convergence of a stochastic diffusion-optimistic learning (DOL) strategy for solving distributed nonconvex (NC) and Polyak–Lojasiewicz (PL) min-max optimization problems. Problems of this type are of interest due to a wide range of applications, including in gene…
This work presents a fully distributed algorithm for learning the optimal policy in a multi-agent cooperative reinforcement learning scenario. We focus on games that can only be solved through coordinated team work. We consider situations in which K players interact simultaneously with an environmen…
This work develops an effective distributed algorithm for the solution of stochastic optimization problems that involve partial coupling among both local constraints and local cost functions. While the collection of networked agents is interested in discovering a global model, the individual agents…