This paper presents Value-Decomposition Networks (VDN) for cooperative multi-agent reinforcement learning (MARL) with a single joint reward signal. The authors address the challenges of cooperative MARL, where agents must jointly optimize a single reward signal in partially observable environments. Traditional approaches, such as fully centralized and decentralized methods, suffer from issues like spurious rewards and the "lazy agent" problem, where one agent becomes inactive while others work. VDNs address these issues by decomposing the team reward into individual agent value functions, allowing agents to learn independently while still coordinating effectively. The VDN architecture trains individual agents to decompose the team reward into agent-specific value functions, which are learned through backpropagation of the total Q-gradient through deep neural networks. This approach avoids spurious reward signals and enables agents to learn effectively even in partially observable environments. The paper evaluates VDNs across various partially observable multi-agent domains and shows that they outperform both centralized and independent learning approaches, especially when combined with techniques like weight sharing, role information, and information channels. The authors also compare VDNs with other approaches, including independent learners and centralized methods, and find that VDNs consistently outperform these methods. The paper highlights the effectiveness of VDNs in complex coordination tasks, such as navigating mazes and playing games like Fetch, Switch, and Checkers. The results show that VDNs can autonomously decompose the joint reward into individual components, leading to better performance and more efficient learning. The study concludes that VDNs represent a significant advancement in cooperative MARL, offering a scalable and effective solution for complex multi-agent learning problems.This paper presents Value-Decomposition Networks (VDN) for cooperative multi-agent reinforcement learning (MARL) with a single joint reward signal. The authors address the challenges of cooperative MARL, where agents must jointly optimize a single reward signal in partially observable environments. Traditional approaches, such as fully centralized and decentralized methods, suffer from issues like spurious rewards and the "lazy agent" problem, where one agent becomes inactive while others work. VDNs address these issues by decomposing the team reward into individual agent value functions, allowing agents to learn independently while still coordinating effectively. The VDN architecture trains individual agents to decompose the team reward into agent-specific value functions, which are learned through backpropagation of the total Q-gradient through deep neural networks. This approach avoids spurious reward signals and enables agents to learn effectively even in partially observable environments. The paper evaluates VDNs across various partially observable multi-agent domains and shows that they outperform both centralized and independent learning approaches, especially when combined with techniques like weight sharing, role information, and information channels. The authors also compare VDNs with other approaches, including independent learners and centralized methods, and find that VDNs consistently outperform these methods. The paper highlights the effectiveness of VDNs in complex coordination tasks, such as navigating mazes and playing games like Fetch, Switch, and Checkers. The results show that VDNs can autonomously decompose the joint reward into individual components, leading to better performance and more efficient learning. The study concludes that VDNs represent a significant advancement in cooperative MARL, offering a scalable and effective solution for complex multi-agent learning problems.