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# Centralized Training with Decentralized Execution

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arXiv Oct 6, 2026
Mirror-IRS-assisted hybrid RF/VLC network: two-agent DRL jointly tunes selection, power and mirror orientation, holding fairness above 0.93
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.
Ahrar N. Hamad（First author）
IRS-Aided Hybrid RF/VLC Systems Cooperative Multi-Agent Deep Reinforcement Learning Intelligent Reflecting Surface Markov Decision Process Centralized Training with Decentralized Execution
International Journal of Innovative Science and Research Technology (IJISRT) Sep 26, 2026
A review maps multi-agent reinforcement learning from Markov games to three algorithm lineages, flagging scale, unfamiliar-partner cooperation and deployment safety as open
This review uses Markov games and their decentralized, partially observed variants as the mathematical frame, groups the multi-agent reinforcement learning literature into three lineages—agents learning in isolation, methods that factor a team value into per-agent pieces (VDN, QMIX and their descendants), and actor-critic schemes trained against a critic with global knowledge (MADDPG, COMA, MAPPO)—discusses the obstacles that distinguish MARL from its single-agent counterpart, namely a moving-target learning problem, dividing a shared reward among team members, limited local views, and growth of the joint action space, explains why training with global information but acting on local information has become the standard design, reviews uses in real-time strategy games, robot teams, automate
This review uses Markov games and their decentralized, partially observed variants as the mathematical frame, groups the multi-agent reinforcement learning literature into three lineages—agents learning in isolation, methods that factor a team value into per-agent pieces (VDN, QMIX and their descendants), and actor-critic schemes trained against a critic with global knowledge (MADDPG, COMA, MAPPO)—discusses the obstacles that distinguish MARL from its single-agent counterpart, namely a moving-target learning problem, dividing a shared reward among team members, limited local views, and growth of the joint action space, explains why training with global information but acting on local information has become the standard design, reviews uses in real-time strategy games, robot teams, automate
Rajiv Ranganath（First author）
Multi-Agent Reinforcement Learning Value Decomposition Policy Gradient Methods Centralized Training with Decentralized Execution

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