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Beyond Adaptive Critic- Creative Learning for Intelligent Mobile Robots

机译:超越自适应批判-智能移动机器人的创造性学习

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Intelligent industrial and mobile robots may be considered proven technology in structured environments. Teach programming and supervised learning methods permit solutions to a variety of applications. However, we believe that to extend the operation of these machines to more unstructured environments requires a new learning method. Both unsupervised learning and reinforcement learning are potential candidates for these new tasks. The adaptive critic method has been shown to provide useful approximations or even optimal control policies to non-linear systems. The purpose of this paper is to explore the use of new learning methods that goes beyond the adaptive critic method for unstructured environments. The adaptive critic is a form of reinforcement learning. A critic element provides only high level grading corrections to a cognition module that controls the action module. In the proposed system the critic's grades are modeled and forecasted, so that an anticipated set of sub-grades are available to the cognition model. The forecasting grades are interpolated and are available on the time scale needed by the action model. The success of the system is highly dependent on the accuracy of the forecasted grades and adaptability of the action module. Examples from the guidance of a mobile robot are provided to illustrate the method for simple line following and for the more complex navigation and control in an unstructured environment. The theory presented that is beyond the adaptive critic may be called creative theory. Creative theory is a form of learning that models the highest level of human learning- imagination. The application of the creative theory appears to not only be to mobile robots but also to many other forms of human endeavor such as educational learning and business forecasting. Reinforcement learning such as the adaptive critic may be applied to known problems to aid in the discovery of their solutions. The significance of creative theory is that it permits the discovery of the unknown problems, ones that are not yet recognized but may be critical to survival or success.
机译:智能工业机器人和移动机器人在结构化环境中可能被认为是成熟的技术。示教编程和监督学习方法可为多种应用提供解决方案。但是,我们认为将这些机器的操作扩展到更多非结构化环境需要一种新的学习方法。无监督学习和强化学习都是这些新任务的潜在候选人。自适应批评家方法已被证明可以为非线性系统提供有用的近似甚至最优控制策略。本文的目的是探索对非结构化环境超越自适应评论家方法的新学习方法的使用。适应性批评家是强化学习的一种形式。评论者元素仅向控制动作模块的认知模块提供高级分级更正。在提出的系统中,对批评者的等级进行建模和预测,以便预期的子等级集可用于认知模型。预测等级是插值的,并且可以在操作模型所需的时间范围内使用。系统的成功在很大程度上取决于预测成绩的准确性和行动模块的适应性。提供了来自移动机器人指导的示例,以说明在非结构化环境中进行简单直线跟踪以及进行更复杂的导航和控制的方法。提出的超出适应性批评家的理论可以称为创造论。创造论是一种模仿人类最高水平的想象力的学习形式。创新理论的应用似乎不仅适用于移动机器人,而且适用于许多其他形式的人类活动,例如教育学习和业务预测。诸如自适应批评家之类的强化学习可以应用于已知问题,以帮助发现其解决方案。创新理论的意义在于,它可以发现未知的问题,这些问题尚未得到认可,但对生存或成功至关重要。

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