ABSTRACT Autonomous navigation in dynamic environment is aprerequisite of the mobile robot to perform tasks, and numerous approaches have been presented, including the supervised learning. Using supervised learning in robot navigation might meet problems, such as inconsistent and noisy data, and high error in training data. Inspired by the advantages of the reinforcement learning, such as no need for desired outputs, many researchers have applied reinforcement learning to robot navigation. This paper presents anovel method to address the robot navigation in different settings, through integrating supervised learning and analogical reinforcement learning into amotivated developmental network. We focus on the effect of the new learning rate on the robot navigation behavior. Experimentally, we show that the effect of internal neurons on the learning rate allows the agent to approach the target and avoid the obstacle as compounding effects of sequential states in static, dynamic, and complex environments. Further, we compare the performance between the emergent developmental network system and asymbolic system, as well as other four reinforcement learning algorithms. These experiments indicate that the reinforcement learning is beneficial for developing desirable behaviors in this set of robot navigation– staying statistically close to its target and away from obstacle.

中文翻译：

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基于神经调节原理的移动机器人目标导向自主导航

摘要动态环境中的自主导航是移动机器人执行任务的先决条件，并且已经提出了许多方法，包括监督学习。在机器人导航中使用监督学习可能会遇到一些问题，例如不一致和嘈杂的数据，以及训练数据的高错误率。受到强化学习优势的启发，例如不需要期望的输出，许多研究人员将强化学习应用于机器人导航。本文通过将监督学习和类比强化学习集成到有动机的发展网络中，提出了一种解决不同环境下机器人导航的新方法。我们关注新学习率对机器人导航行为的影响。实验上，我们表明内部神经元对学习率的影响允许代理接近目标并避开障碍物作为静态、动态和复杂环境中顺序状态的复合效应。此外，我们比较了新兴发展网络系统和非符号系统以及其他四种强化学习算法之间的性能。这些实验表明，强化学习有利于在这组机器人导航中开发理想的行为——在统计上保持接近其目标并远离障碍物。以及其他四种强化学习算法。这些实验表明，强化学习有利于在这组机器人导航中开发理想的行为——在统计上保持接近其目标并远离障碍物。以及其他四种强化学习算法。这些实验表明，强化学习有利于在这组机器人导航中开发理想的行为——在统计上保持接近其目标并远离障碍物。