Abstract
Reinforcement learning is an efficient, widely used machine learning technique that performs well in problems that are characterized by a small number of states and actions. This is rarely the case in multiagent learning problems. For the multiagent case, standard approaches may not be adequate. As an alternative, it is possible to use techniques that generalize the state space to allow agents to learn through the use of abstractions. Thus, the focus of this work is to combine multiagent learning with a generalization technique, namely tile coding. This kind of method is key in scenarios where agents have a high number of states to explore. In the scenarios used to test and validate this approach, our results indicate that the proposed representation outperforms the tabular one and is then an effective alternative.
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References
Bazzan, A.L.C.: Opportunities for multiagent systems and multiagent reinforcement learning in traffic control. Autonomous Agents and Multiagent Systems 18(3), 342–375 (2009)
Claus, C., Boutilier, C.: The dynamics of reinforcement learning in cooperative multiagent systems. In: Proceedings of the Fifteenth National Conference on Artificial Intelligence, pp. 746–752 (1998)
Guestrin, C., Lagoudakis, M.G., Parr, R.: Coordinated reinforcement learning. In: Proceedings of the Nineteenth International Conference on Machine Learning (ICML), pp. 227–234. Morgan Kaufmann, San Francisco (2002)
Kok, J., Vlassis, N.: Collaborative multiagent reinforcement learning by payoff propagation. Journal of Machine Learning Research 7, 1789–1828 (2006)
Oliveira, D.d., Bazzan, A.L.C.: Multiagent learning on traffic lights control: effects of using shared information. In: Bazzan, A.L.C., Klügl, F. (eds.) Multi-Agent Systems for Traffic and Transportation, pp. 307–321. IGI Global, Hershey (2009)
Sherstov, A.A., Stone, P.: Improving action selection in MDP’s via knowledge transfer. In: Proceedings of the Twentieth National Conference on Artificial Intelligence (July 2005)
Sutton, R., Barto, A.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
Sutton, R.S.: Generalization in reinforcement learning: Successful examples using sparse coding. In: Touretzky, D., Mozer, M., Hasselmo, M. (eds.) Advances in Neural Information Processing Systems, vol. 8, pp. 1038–1044. MIT Press, Cambridge (1996)
Waskow, S.J., Bazzan, A.L.C.: Reinforcement learning methods: Generalizing joint tasks. In: Proceedings of the 35th Latin-American Informatics Conference, CLEI, Pelotas, Brazil (September 2009)
Watkins, C.: Learning from Delayed Rewards. PhD thesis, University of Cambridge (1989)
Whiteson, S., Taylor, M.E., Stone, P.: Adaptive tile coding for value function approximation. Technical Report AI-TR-07-339, University of Texas at Austin (2007)
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Waskow, S.J., Bazzan, A.L.C. (2010). Improving Space Representation in Multiagent Learning via Tile Coding. In: da Rocha Costa, A.C., Vicari, R.M., Tonidandel, F. (eds) Advances in Artificial Intelligence – SBIA 2010. SBIA 2010. Lecture Notes in Computer Science(), vol 6404. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16138-4_16
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DOI: https://doi.org/10.1007/978-3-642-16138-4_16
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