  In distributed optimization, the practical problem-solving performance is
essentially sensitive to algorithm selection, parameter setting, problem type
and data pattern. Thus, it is often laborious to acquire a highly efficient
method for a given specific problem. In this paper, we propose a learning-based
method to achieve efficient distributed optimization over networked systems.
Specifically, a deep reinforcement learning (DRL) framework is developed for
adaptive configuration within a parameterized unifying algorithmic form, which
incorporates an abundance of decentralized first-order and second-order
optimization algorithms. We exploit the local consensus and objective
information to represent the regularities of problem instances and trace the
solving progress, which constitute the states observed by a DRL agent. The
framework is trained using Proximal Policy Optimization (PPO) on a number of
practical problem instances of similar structures yet different problem data.
Experiments on various smooth and non-smooth classes of objective functions
demonstrate that our proposed learning-based method outperforms several
state-of-the-art distributed optimization algorithms in terms of convergence
speed and solution accuracy.
