Abstract
A algorithm of dynamic multi-step reinforcement learning based on virtual potential field path planning is proposed in this paper. Firstly, it is constructed the virtual potential field according to the known information. And then in view of \( Q \) learning algorithm of the \( Q\left( \lambda \right) \) algorithm, a multi-step reinforcement learning algorithm is proposed in this paper. It can update current \( Q \) value used of future dynamic \( k \) steps according to the current environment status. At the same time, the convergence is analyzed. Finally the simulation experiments are done. It shows that the proposed algorithm and convergence and so on are more efficiency than similar algorithms.
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Acknowledgements
This work was supported in part by national natural science foundation of china (61602187), Science and Guangdong science and technology project (2016A040403122), Guangzhou science and technology project-science research project, (201707010482).
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Liu, J., Qi, W., Lu, X. (2017). Multi-step Reinforcement Learning Algorithm of Mobile Robot Path Planning Based on Virtual Potential Field. In: Zou, B., Han, Q., Sun, G., Jing, W., Peng, X., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 728. Springer, Singapore. https://doi.org/10.1007/978-981-10-6388-6_45
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DOI: https://doi.org/10.1007/978-981-10-6388-6_45
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