An internal model of autonomous mobile robots (agent) is proposed in this paper. A TSK-type fuzzy net is used as a classifier of environment information, i.e., the state of an agent, and reinforcement learning methods such as Q-learning, sarsa-learning are used to make multiple agents acquire adaptive behaviors. Goal navigated exploration problem was simulated to confirm the effectiveness of the proposed methods, and the results showed that the new learning methods are more efficient than actor-critic method which was proposed by our previous work. © Springer-Verlag 2012.
CITATION STYLE
Kuremoto, T., Yamano, Y., Feng, L. B., Kobayashi, K., & Obayashi, M. (2012). A neuro-fuzzy network with reinforcement learning algorithms for swarm learning. In Lecture Notes in Electrical Engineering (Vol. 144 LNEE, pp. 101–108). https://doi.org/10.1007/978-3-642-27326-1_14
Mendeley helps you to discover research relevant for your work.