Abstract
This paper introduces a hybrid reinforcement learning algorithm for robot obstacle avoidance. This algorithm is based on SARSA (λ), and mix with the supervised learning. This hybrid learning algorithm can reduce the learning time obviously which is demonstrated by the simulations. In reinforcement learning, generalization of learning space is important for learning efficiency. An uneven generalization model is designed for improving the learning efficiency. The simulations show that the uneven model can not only reduce the learning time, but also the moving steps.
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Acknowledgments
This work is supported by Hebei Province Natural Science Foundation for Youth (No. F2011203065), Major Basic Research Program of Applied Basic Research Project of Hebei Province Science and Technology R&D Project (No. 11963545D), and Qinhuangdao City Science and Technology R&D Project (No. 2012021A31).
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Li, J., Bi, W., Li, M. (2013). Hybrid Reinforcement Learning and Uneven Generalization of Learning Space Method for Robot Obstacle Avoidance. In: Sun, Z., Deng, Z. (eds) Proceedings of 2013 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 255. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38460-8_20
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DOI: https://doi.org/10.1007/978-3-642-38460-8_20
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