Abstract
An modeling other agents (MOA) constructs a model of other agents in every agent. It enables the agents to predict the actions of other agents and achieve coordinated and effective interactions in multi-agent systems. However, the relationship between the executed and predicted actions of agents is vague and diverse. To clarify the relationship, we proposed a method by which an agent through communications constructs its MOA using the historical data of other agents and asymmetrically treats itself and its MOA in a non-cooperative game to obtain Stackelberg equilibrium (SE). Subsequently, the SE are used to choose actions. We experimentally demonstrated that, in a partially observable and mixed cooperative-competitive environment, agents using our method with reinforcement learning could establish better coordination and engage in behaviors that are more appropriate compared to conventional methods. We then analyzed the coordinated interaction structure generated in the trained network to clarify the relationship between individual agents.
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References
Albrecht, S.V., Stone, P.: Autonomous agents modelling other agents: a comprehensive survey and open problems. Artif. Intell. 258, 66–95 (2018)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1724–1734 (2014)
Haarnoja, T., Tang, H., Abbeel, P., Levine, S.: Reinforcement learning with deep energy-based policies. In: International Conference on Machine Learning, pp. 1352–1361. PMLR (2017)
He, H., Boyd-Graber, J., Kwok, K., Daumé III, H.: Opponent modeling in deep reinforcement learning. In: International Conference on Machine Learning, pp. 1804–1813. PMLR (2016)
Hong, Z.W., Su, S.Y., Shann, T.Y., Chang, Y.H., Lee, C.Y.: A deep policy inference Q-network for multi-agent systems. arXiv preprint arXiv:1712.07893 (2017)
Hu, J., Wellman, M.P.: Nash Q-learning for general-sum stochastic games. J. Mach. Learn. Res. 4, 1039–1069 (2003)
Huhns, M.N.: Distributed Artificial Intelligence: Volume I. Elsevier (1987)
Jaques, N., et al.: Social influence as intrinsic motivation for multi-agent deep reinforcement learning. In: International Conference on Machine Learning, pp. 3040–3049. PMLR (2019)
Jiang, J., Lu, Z.: Learning attentional communication for multi-agent cooperation. In: Advances in Neural Information Processing Systems, vol. 31 (2018)
Lanctot, M., et al.: A unified game-theoretic approach to multiagent reinforcement learning. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Littman, M.L.: Markov games as a framework for multi-agent reinforcement learning. In: Machine Learning Proceedings 1994, pp. 157–163. Elsevier (1994)
Lowe, R., Wu, Y.I., Tamar, A., Harb, J., Pieter Abbeel, O., Mordatch, I.: Multi-agent actor-critic for mixed cooperative-competitive environments. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Peng, P., et al.: Multiagent bidirectionally-coordinated nets: emergence of human-level coordination in learning to play StarCraft combat games. arXiv preprint arXiv:1703.10069 (2017)
Rashid, T., Samvelyan, M., De Witt, C.S., Farquhar, G., Foerster, J., Whiteson, S.: Monotonic value function factorisation for deep multi-agent reinforcement learning. J. Mach. Learn. Res. 21(1), 1–51 (2020)
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)
Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Sig. Process. 45(11), 2673–2681 (1997)
Semsar-Kazerooni, E., Khorasani, K.: Multi-agent team cooperation: a game theory approach. Automatica 45(10), 2205–2213 (2009)
Sinha, A., Malo, P., Deb, K.: A review on bilevel optimization: from classical to evolutionary approaches and applications. IEEE Trans. Evol. Comput. 22(2), 276–295 (2017)
Sukhbaatar, S., Fergus, R., et al.: Learning multiagent communication with backpropagation. In: Advances in Neural Information Processing Systems, vol. 29 (2016)
Tuyls, K., Weiss, G.: Multiagent learning: basics, challenges, and prospects. AI Mag. 33(3), 41 (2012)
Von Stackelberg, H.: Market Structure and Equilibrium. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12586-7
Wang, X., et al.: SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II. In: International Conference on Machine Learning, pp. 10905–10915. PMLR (2021)
Yang, Y., Wang, J.: An overview of multi-agent reinforcement learning from game theoretical perspective. arXiv preprint arXiv:2011.00583 (2020)
Zhang, H., et al.: Bi-level actor-critic for multi-agent coordination. In: The 34th AAAI Conference on Artificial Intelligence, pp. 7325–7332. AAAI Press (2020)
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Zhong, J., Sugawara, T. (2023). Modeling Others as a Player in Non-cooperative Game for Multi-agent Coordination. In: Iliadis, L., Maglogiannis, I., Alonso, S., Jayne, C., Pimenidis, E. (eds) Engineering Applications of Neural Networks. EANN 2023. Communications in Computer and Information Science, vol 1826. Springer, Cham. https://doi.org/10.1007/978-3-031-34204-2_42
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