Abstract
Multi-agent reinforcement learning for multi-robot systems is a challenging issue in both robotics and artificial intelligence. But multi-agent reinforcement learning is bedeviled by the curse of dimensionality. In this paper, a novel hierarchical reinforcement learning approach named MOMQ is presented for multi-robot cooperation. The performance of MOMQ is demonstrated in three-robot trash collection task.
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Cao, Y.U., Fukunaga, A.S., Kahng, A.B.: Cooperative mobile robotics: antecedents and directions. Autonomous Robots 4, 1–23 (1997)
Fernandez, F., Parker, L.E.: Learning in large cooperative multi-robot domains. International Journal of Robotics and Automation 16(4), 217–226 (2001)
Touzet, C.F.: Distributed lazy Q-learning for cooperative mobile robots. International Journal of Advanced Robotic Systems 1(1), 5–13 (2004)
Yang, E., Gu, D.: Multiagent Reinforcement Learning for Multi-Robot Systems: A Survey. Technical Report CSM-404, University of Essex (2004)
Barto, A.G., Mahadevan, S.: Recent advances in hierarchical reinforcement learning. Discrete Event Dynamic Systems: Theory and Applications 13(4), 41–77 (2003)
Sutton, R., Precup, D., Singh, S.: Between MDPs and Semi-MDPs: A framework for temporal abstraction in reinforcement learning. Artificial Intelligence 112, 181–211 (1999)
Parr, R.: Hierarchical control and learning for Markov decision processes. PhD dissertation, University of California at Berkeley (1998)
Dietterich, T.: Hierarchical reinforcement learning with the MAXQ value function decomposition. Journal of Artificial Intelligence Research 13, 227–303 (2000)
Ghavamzadeh, M., Mahadevan, S., Makar, R.: Hierarchical multiagent reinforcement learning. Journal of Autonomous Agents and Multi-Agent Systems 13, 197–229 (2006)
de Castro, L.N., Von Zuben, F.N.: An evolutionary immune network for data clustering. In: Proceedings of the IEEE Brazilian Symposium on Artificial Neural Networks, Rio de Janeiro, Brazil, vol. 1, pp. 84–89 (2000)
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Cheng, X., Shen, J., Liu, H., Gu, G. (2007). Multi-robot Cooperation Based on Hierarchical Reinforcement Learning. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds) Computational Science – ICCS 2007. ICCS 2007. Lecture Notes in Computer Science, vol 4489. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72588-6_12
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DOI: https://doi.org/10.1007/978-3-540-72588-6_12
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