Reinforcement learning for fuzzy agents: application to a pighouse environment control
New learning paradigms in soft computing
Layered learning in multiagent systems
Layered learning in multiagent systems
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Fuzzy inference system learning by reinforcement methods
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A case-based approach for coordinated action selection in robot soccer
Artificial Intelligence
Iterative learning control based tools to learn from human error
Engineering Applications of Artificial Intelligence
Reinforcement learning in robotics: A survey
International Journal of Robotics Research
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The robot soccer game has been proposed as a benchmark problem for the artificial intelligence and robotic researches. Decision-making system is the most important part of the robot soccer system. As the environment is dynamic and complex, one of the reinforcement learning (RL) method named FNN-RL is employed in learning the decision-making strategy. The FNN-RL system consists of the fuzzy neural network (FNN) and RL. RL is used for structure identification and parameters tuning of FNN. On the other hand, the curse of dimensionality problem of RL can be solved by the function approximation characteristics of FNN. Furthermore, the residual algorithm is used to calculate the gradient of the FNN-RL method in order to guarantee the convergence and rapidity of learning. The complex decision-making task is divided into multiple learning subtasks that include dynamic role assignment, action selection, and action implementation. They constitute a hierarchical learning system. We apply the proposed FNN-RL method to the soccer agents who attempt to learn each subtask at the various layers. The effectiveness of the proposed method is demonstrated by the simulation and the real experiments.