Abstract
We described a new preteaching method for re-inforcement learning using a self-organizing map (SOM). The purpose is to increase the learning rate using a small amount of teaching data generated by a human expert. In our proposed method, the SOM is used to generate the initial teaching data for the reinforcement learning agent from a small amount of teaching data. The reinforcement learning function of the agent is initialized by using the teaching data generated by the SOM in order to increase the probability of selecting the optimal actions it estimates. Because the agent can get high rewards from the start of reinforcement learning, it is expected that the learning rate will increase. The results of a mobile robot simulation showed that the learning rate had increased even though the human expert had showed only a small amount of teaching data.
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References
Sutton RS, Barto A (1998) Reinforcement learning: an introduction. A Bradford Book, MIT Press, Cambridge
Kohonen T (1996) Self-organizing maps (in Japanese) Springer, Berlin Tokyo
Clouse JA, Utgoff PE (1992) A teaching method for reinforcement learning. Proceedings of the 9th International Conference on Machine Learning, p 92–101
Lin L-J (1991) Programming robots using reinforcement learning and teaching. Proceedings of the Ninth National Conference on Artificial Intelligence, p 781–786
Watkins CJCH, Dayan P (1992) Technical note: Q-learning. Mach Learn 8:55–68
Sutton RS (1996) Generalization in reinforcement learning: successful examples using space coarse coding. Adv Neural Inf Process Syst 8:1038–1044
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This work was presented in part at the 7th International Symposium on Artificial Life and Robotics, Oita, Japan, January 16–18, 2002
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Tateyama, T., Kawata, S. & Oguchi, T. A teaching method using a self-organizing map for reinforcement learning. Artificial Life and Robotics 7, 193–197 (2004). https://doi.org/10.1007/BF02471206
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DOI: https://doi.org/10.1007/BF02471206