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k -certainty exploration method, an efficient reinforcement learning algorithm, is not applied to environments whose state space is continuous because continuous state space must be changed to discrete state space. Our purpose is to construct discrete semi-Markov decision process (SMDP) models of such environments using growing cell structures to autonomously divide continuous state space then using k -certainty exploration method to construct SMDP models. Multiagent k -certainty exploration method is then used to improve exploration efficiency. Mobile robot simulation demonstrated our proposal's usefulness and efficiency.

journal of advanced computational intelligence and intelligent informatics

Construction of Semi-Markov Decision Process Models of Continuous State Space Environments Using Growing Cell Structures and Multiagentk-Certainty Exploration Method