• M.Seetharama Prasad

Reinforcement learning has become a potent tool for decision-making problems in complex and dynamic environments. Applied to MAS, which interact in complex networks, it has significant potential to help solve some real-life problems resulting from domains such as communication networks, smart grids, and traffic management. MARL draws on the concept of enabling agents to learn optimal behaviors through interacting with the environment and other agents. However, the complexity of the system drastically increases with certain features like partial observability, non-stationarity of environment, and the need for coordination or competition among agents. The design of effective RL algorithms for MAS involves referring to important issues such as scalability, stability of learning, and reward sharing mechanisms. MARL algorithms can be used to optimize decentralized decision-making and improve overall efficiency in complex networks where nodes are agents and edges capture their dynamic interactions. Advanced techniques, such as hierarchical reinforcement learning, graph-based RL, and reward shaping, are typically used to improve the learning performance in a networked environment. In addition, mechanisms such as policy-sharing, multi-agent credit assignment, and communication protocols between agents will be important for efficient coordination. Recent advances in reinforcement learning for MAS in complex networks are explored in this paper by analyzing various architectures and algorithms and pointing out their applicability in different network scenarios. It also discusses open challenges, including convergence guarantees, computational efficiency, and fairness in multi-agent coordination. Finally, this study concludes by pinpointing some potential research directions that can further enhance the applicability of MARL in real-world networked systems.

Multi-agent reinforcement learning, complex networks, decentralized decision-making, coordination, non-stationarity, scalability, hierarchical reinforcement learning, graph-based RL, policy-sharing, multi-agent credit assignment.

"Exploring Multi-Agent Reinforcement Learning for Complex Network Dynamics ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 6, page no.i721-i743, June-2025, Available :http://www.jetir.org/papers/JETIR2506885.pdf

"Exploring Multi-Agent Reinforcement Learning for Complex Network Dynamics ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 6, page no. ppi721-i743, June-2025, Available at : http://www.jetir.org/papers/JETIR2506885.pdf