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Improving Recurrent CSVM Performance for Robot Navigation on Discrete Labyrinths

机译:改善离散迷宫机器人导航的递归CSVM性能

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This paper presents an improvement of a recurrent learning system called LSTM-CSVM (introduced in [1]) for robot navigation applications, this approach is used to deal with some of the main issues addressed in the research area: the problem of navigation on large domains, partial observability, limited number of learning experiences and slow learning of optimal policies. The advantages of this new version of LSTM-CSVM system, are that it can find optimal paths through mazes and it reduces the number of generations to evolve the system to find the optimal navigation policy, therefore either the training time of the system is reduced. This is done by adding an heuristic methodoly to find the optimal path from start state to the goal state.can contain information about the whole environment or just partial information about it.
机译:本文提出了一种针对LSTM-CSVM(在[1]中引入)的递归学习系统的改进,该系统用于机器人导航应用,该方法用于处理研究领域中解决的一些主要问题:大型导航问题。领域,部分可观察性,学习经验数量有限以及最佳政策的缓慢学习。这种新版本的LSTM-CSVM系统的优点在于,它可以通过迷宫找到最佳路径,并且减少了进化系统以找到最佳导航策略的代数,因此减少了系统的训练时间。这是通过添加启发式方法来找到从开始状态到目标状态的最佳路径来完成的。它可以包含有关整个环境的信息,也可以仅包含有关该环境的部分信息。

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