Abstract
We present a model of Reinforcement Learning, which consists of modified neural-network architecture with spatio-temporal connections, known as Temporal Hebbian Self-Organizing Map (THSOM). A number of experiments were conducted to test the model on the maze solving problem. The algorithm demonstrates sustainable learning, building a near to optimal routes. This work describes an agents behavior in the mazes of different complexity and also influence of models parameters at the length of formed paths.
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Acknowledgements
The reported study was supported by RFBR, research Projects No. 16-37-60055 and No. 15-07-06214.
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Filin, D., Panov, A.I. (2018). Applying a Neural Network Architecture with Spatio-Temporal Connections to the Maze Exploration. In: Samsonovich, A., Klimov, V. (eds) Biologically Inspired Cognitive Architectures (BICA) for Young Scientists. BICA 2017. Advances in Intelligent Systems and Computing, vol 636. Springer, Cham. https://doi.org/10.1007/978-3-319-63940-6_8
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DOI: https://doi.org/10.1007/978-3-319-63940-6_8
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