Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Smooth trajectory tracking of three-link robot: a self-organizingCMAC approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
COOPERATIVE LEARNING BY POLICY-SHARING IN MULTIPLE AGENTS
Cybernetics and Systems
Cooperation between multiple agents based on partially sharing policy
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
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In general, Q-learning needs well-defined quantized state spaces and action spaces to obtain an optimal policy for accomplishing a given task. This makes it difficult to be applied to real robot tasks because of poor performance of learned behavior due to the failure of quantization of continuous state and action spaces. In this paper, we proposed a fuzzy-based CMAC method to calculate the contribution of each neighboring state to generate a continuous action value in order to make motion smooth and effective. A momentum term to speed up training has been designed and implemented in a multi-agent system for real robot applications.