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Adaptability analysis of genetic network programming with reinforcement learning in dynamically changing environments

机译：在动态变化的环境中通过强化学习进行遗传网络编程的适应性分析

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摘要

Genetic network programming (GNP) has been proposed as one of the evolutionary algorithms and extended with reinforcement learning (GNP-RL). The combination of evolution and learning can efficiently evolve programs and the fitness improvement has been confirmed in the simulations of tileworld problems, elevator group supervisory control systems, stock trading models and wall following behavior of Khepera robot. However, its adaptability in testing environments, where the situations dynamically change, has not been analyzed in detail yet. In this paper, the adaptation mechanism in the testing environment is introduced and it is confirmed that GNP-RL can adapt to the environmental changes using a robot simulator WEBOTS, especially when unexperienced sensor troubles suddenly occur. The simulation results show that GNP-RL works well in the testing even if wrong sensor information is given because GNP-RL has a function to automatically change programs using alternative actions. In addition, the analysis on the effects of the parameters of GNP-RL is carried out in both training and testing simulations.
机译：遗传网络编程（GNP）已被提出作为一种进化算法，并通过强化学习（GNP-RL）进行了扩展。进化与学习的结合可以有效地进化程序，并且在Khepera机器人的瓷砖世界问题，电梯群监督控制系统，股票交易模型和墙追随行为的仿真中已经证实了适应性的提高。但是，其在动态变化的测试环境中的适应性尚未得到详细分析。本文介绍了测试环境中的适应机制，并证实了GNP-RL可以使用机器人模拟器WEBOTS适应环境变化，尤其是在突然出现无经验的传感器故障时。仿真结果表明，即使给出了错误的传感器信息，GNP-RL仍可在测试中很好地工作，因为GNP-RL具有使用替代动作自动更改程序的功能。此外，在训练和测试模拟中都对GNP-RL参数的影响进行了分析。

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