Model-Free Unsupervised Learning for Optimization Problems with Constraints
This addresses optimization challenges in domains like wireless communications, but appears incremental as it builds on existing neural network and reinforcement learning methods.
The paper tackles constrained optimization problems where objective functions or constraints are difficult to derive by proposing a model-free unsupervised learning framework using neural networks, and demonstrates its efficiency with numerical simulations on a power control problem.
In many optimization problems in wireless communications, the expressions of objective function or constraints are hard or even impossible to derive, which makes the solutions difficult to find. In this paper, we propose a model-free learning framework to solve constrained optimization problems without the supervision of the optimal solution. Neural networks are used respectively for parameterizing the function to be optimized, parameterizing the Lagrange multiplier associated with instantaneous constraints, and approximating the unknown objective function or constraints. We provide learning algorithms to train all the neural networks simultaneously, and reveal the connections of the proposed framework with reinforcement learning. Numerical and simulation results validate the proposed framework and demonstrate the efficiency of model-free learning by taking power control problem as an example.