Autonomous agent based on reinforcement learning and adaptive shadowed network

Bojan Jerbć, Katarina Grolinger, Božo Vranjš
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引用次数: 19

Abstract

The planning of intelligent robot behavior plays an important role in the development of flexible automated systems. The robot’s intelligence comprises its capability to act in unpredictable and chaotic situations, which requires not just a change but the creation of the robot’s working knowledge. Planning of intelligent robot behavior addresses three main issues: finding task solutions in unknown situations, learning from experience and recognizing the similarity of problem paradigms. This article outlines a planning system which integrates the reinforcement learning method and a neural network approach with the aim to ensure autonomous robot behavior in unpredictable working conditions.

The assumption is that the robot is a tabula rasa and has no knowledge of the work space structure. Initially, it has just basic strategic knowledge of searching for solutions, based on random attempts, and a built-in learning system. The reinforcement learning method is used here to evaluate robot behavior and to induce new, or improve the existing, knowledge. The acquired action (task) plan is stored as experience which can be used in solving similar future problems. To provide the recognition of problem similarities, the Adaptive Fuzzy Shadowed neural network is designed. This novel network concept with a fuzzy learning rule and shadowed hidden layer architecture enables the recognition of slightly translated or rotated patterns and does not forget already learned structures.

The intelligent planning system is simulated using object-oriented techniques and verified on planned and random examples, proving the main advantages of the proposed approach: autonomous learning, which is invariant with regard to the order of training samples, and single iteration learning progress.

基于强化学习和自适应阴影网络的自主智能体
智能机器人的行为规划在柔性自动化系统的发展中起着重要的作用。机器人的智能包括它在不可预测和混乱的情况下采取行动的能力,这不仅需要改变,还需要创造机器人的工作知识。智能机器人行为规划主要解决三个问题:在未知情况下寻找任务解决方案、从经验中学习和识别问题范式的相似性。本文概述了一种集成强化学习方法和神经网络方法的规划系统,旨在确保机器人在不可预测的工作条件下的自主行为。假设机器人是一个白板,不知道工作空间结构。最初,它只有基于随机尝试寻找解决方案的基本战略知识,以及一个内置的学习系统。这里使用强化学习方法来评估机器人的行为,并诱导新的或改进现有的知识。获得的行动(任务)计划被储存为经验,可用于解决未来类似的问题。为了提供问题相似度的识别,设计了自适应模糊阴影神经网络。这种新颖的网络概念具有模糊学习规则和阴影隐藏层架构,可以识别轻微平移或旋转的模式,并且不会忘记已经学习的结构。利用面向对象技术对智能规划系统进行了仿真,并在计划样例和随机样例上进行了验证,证明了该方法的主要优点:自主学习,训练样本顺序不变,单迭代学习进度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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