多机器人环境下双机器人协作:一种应用q学习方法

Bandita Sahu, P. K. Das, M. R. Kabat
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引用次数: 2

摘要

本文提出了一种应用经典q -学习和改进q -学习算法来执行孪生机器人作业的新方法。该方法对算法执行的空间和时间要求显著降低,因为每个状态只存储具有匹配动作的最佳q值,从而减少了对空间的要求。类似地,只执行q值最好的状态-动作对,所需的执行时间更少。将经典的q -0学习和改进的q -0学习应用于孪生机器人的操作中,该算法在空间要求和遍历时间方面具有优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Twin Robot Cooperation in Multi-Robot Environment: An Applied Q-learning
This paper provides a new approach of executing the twin robot operation with the application of classical Q-learning and improved Q-learning algorithm. This approach has significantly less space and time requirement for execution of the algorithm As only the best Q-value with matched actions are stored for each state, the space requirement is reduced. Similarly, only the best matched state-action pairs with best Q-value are executed, it requires less amount of time for execution. On application of the classical and improved Q-0learning in twin robot operation, the proposed algorithm takes the supremacy with respect to the space requirement and the traversal time.
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