Design and simulation of a path decision algorithm for a labyrinth robot using neural networks
2022 IEEE Biennial Congress of Argentina (ARGENCON)(2022)
摘要
This document consists of the design and simulation of a decision algorithm capable of solving labyrinths using neural networks under the sequential and LSTM recursive architec-tures.For this design, important elements are mentioned for the Q-learning algorithm and an Adam optimization algorithm through the MSE loss function. There are two types of charac-teristics: qualitative and quantitative, the first because Python need compatible libraries such as the following: Numpy, PyTorch, Matplotlib, Tkinter and Jupiter. The second is quantitative type for the construction of the Q-learning algorithm based on the states and actions that the agent performs to define the reward according to the short and long deadline objectives. Labyrinths are generated randomly so that the agent does not memorize the solution path for which tests were made and test the training of each neural network.
更多查看译文
关键词
deep reinforcement learning,Q learning,labyrinth,sequential neural network,recursive neural network
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要