Comparative Analysis of Reinforcement Learning Architectures in Multi-Agent Environments for Coordinated Problem Solving
Keywords:
Reinforcement Learning, Multi-Agent Systems, Centralized Architectures, Decentralized Architectures, Hybrid Frameworks, Cooperative Problem SolvingAbstract
Reinforcement Learning (RL) has seen significant advancements in multi-agent environments, particularly for coordinated problem-solving tasks. This study provides a comparative analysis of key RL architectures, including centralized, decentralized, and hybrid frameworks, examining their effectiveness in scenarios requiring cooperation, competition, or mixed behaviors among agents. We evaluate these architectures across various metrics, including scalability, learning efficiency, and adaptability, highlighting trade-offs in their design and implementation. Additionally, the role of communication protocols, reward mechanisms, and policy-sharing strategies are explored to understand their influence on system performance. This analysis serves as a foundation for optimizing RL models in multi-agent systems, providing insights into their applicability across domains such as robotics, traffic management, and distributed computing.
References
Busoniu, L., Babuska, R., & De Schutter, B. (2008). A comprehensive survey of multi-agent reinforcement learning. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(2), 156–172.
Foerster, J., Assael, Y. M., de Freitas, N., & Whiteson, S. (2016). Learning to communicate with deep multi-agent reinforcement learning. Advances in Neural Information Processing Systems (NeurIPS), 29, 2137–2145.
Kraemer, L., & Banerjee, B. (2016). Multi-agent reinforcement learning as a rehearsal for decentralized planning. Neurocomputing, 190, 82–94.
Lowe, R., Wu, Y., Tamar, A., Harb, J., Abbeel, P., & Mordatch, I. (2017). Multi-agent actor-critic for mixed cooperative-competitive environments. Advances in Neural Information Processing Systems (NeurIPS), 30, 6379–6390.
Tan, M. (1993). Multi-agent reinforcement learning: Independent vs. cooperative agents. Proceedings of the Tenth International Conference on Machine Learning (ICML), 330–337.
Zhang, C., & Lesser, V. R. (2013). Coordinated multi-agent reinforcement learning in networked distributed POMDPs. Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI), 1, 764–770.
Shoham, Y., Powers, R., & Grenager, T. (2007). If multi-agent learning is the answer, what is the question? Artificial Intelligence, 171(7), 365–377.
Panait, L., & Luke, S. (2005). Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems, 11(3), 387–434.
Gupta, J. K., Egorov, M., & Kochenderfer, M. (2017). Cooperative multi-agent control using deep reinforcement learning. Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 66–83.
Matignon, L., Laurent, G. J., & Le Fort-Piat, N. (2012). Independent reinforcement learners in cooperative Markov games: A survey regarding coordination problems. The Knowledge Engineering Review, 27(1), 1–31.
