# Cooperative Multi-Agent Deep Reinforcement Learning

For an indoor IRS-assisted hybrid RF/VLC downlink network, this work formulates joint proportional-fairness maximization over RF/VLC technology selection, power allocation and IRS mirror roll/yaw orientation as an MDP and solves it with a cooperative two-agent DRL framework under centralized training with decentralized execution, where one agent handles selection and power and the other handles mirror orientation; simulations converge to 350 Mbps, within 10% of the 392 Mbps exhaustive-search bound, outperform DDPG and DQN, and keep Jain's fairness index above 0.93 as user count grows.

For an indoor IRS-assisted hybrid RF/VLC downlink network, this work formulates joint proportional-fairness maximization over RF/VLC technology selection, power allocation and IRS mirror roll/yaw orientation as an MDP and solves it with a cooperative two-agent DRL framework under centralized training with decentralized execution, where one agent handles selection and power and the other handles mirror orientation; simulations converge to 350 Mbps, within 10% of the 392 Mbps exhaustive-search bound, outperform DDPG and DQN, and keep Jain's fairness index above 0.93 as user count grows.