@inproceedings{a1c43fa6711744dbb80e5930e23668da,
title = "Behavior Acquisition on a Mobile Robot Using Reinforcement Learning with Continuous State Space",
abstract = "In the application of Reinforcement Learning to real tasks, the construction of state space is a significant problem. In order to use in the real-world environment, we need to deal with the problem of continuous information. Therefore, we proposed a method of the construction of state space using Growing Neural Gas. In our method, the agent constructs a state space model from its own experience autonomously. Furthermore, it can reconstruct the suitable state space model to adapt the complication of the environment. Through the experiments, we showed that Reinforcement Learning could be performed efficiently by successively updating the state space model according to the environment.",
keywords = "Machine learning, Mobile robot, Reinforcement learning, Self-Organization, State space",
author = "Tomoyuki Arai and Yuichiro Toda and Naoyuki Kubota",
year = "2019",
month = jul,
doi = "10.1109/ICMLC48188.2019.8949181",
language = "English",
series = "Proceedings - International Conference on Machine Learning and Cybernetics",
publisher = "IEEE Computer Society",
booktitle = "Proceedings of 2019 International Conference on Machine Learning and Cybernetics, ICMLC 2019",
address = "United States",
note = "18th International Conference on Machine Learning and Cybernetics, ICMLC 2019 ; Conference date: 07-07-2019 Through 10-07-2019",
}