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For a multi-cell, multi-user, cellular network downlink sum-rate maximizationthrough power allocation is a nonconvex and NP-hard optimization problem. Inthis paper, we present an effective approach to solving this problem throughsingle- and multi-agent actor-critic deep reinforcement learning (DRL).Specifically, we use finite-horizon trust region optimization. Throughextensive simulations, we show that we can simultaneously achieve higherspectral efficiency than state-of-the-art optimization algorithms like weightedminimum mean-squared error (WMMSE) and fractional programming (FP), whileoffering execution times more than two orders of magnitude faster than theseapproaches. Additionally, the proposed trust region methods demonstratesuperior performance and convergence properties than the Advantage Actor-Critic(A2C) DRL algorithm. In contrast to prior approaches, the proposeddecentralized DRL approaches allow for distributed optimization with limitedCSI and controllable information exchange between BSs while offeringcompetitive performance and reduced training times.

Centralized & Distributed Deep Reinforcement Learning Methods for Downlink Sum-Rate Optimization