Genetic Network Programming with Reinforcement Learning and Its Application to Making Mobile Robot Behavior

Mabu, Shingo; Hatakeyama, Hiroyuki; Thu, Moe Thu; Hirasawa, Kotaro; Hu, Jinglu

doi:10.1541/ieejeiss.126.1009

Cited by 17 publications

(5 citation statements)

References 5 publications

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“…The experimentally obtained results demonstrated that their method presents advantages over conventional methods based on GP and EP. Moreover, Chen et al [20] combined GNP with Sarsa learning [21] for stock market trading to judge the timing of buying and selling. The experimentally obtained simulation results obtained using stock price datasets of 16 brands during four years clarified that the fitness and profits of their composed method were higher than the existing stock prediction methods.…”

Section: Related Studiesmentioning

confidence: 99%

Automatic Calibration of Piezoelectric Bed-Leaving Sensor Signals Using Genetic Network Programming Algorithms

2021

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This paper presents a filter generating method that modifies sensor signals using genetic network programming (GNP) for automatic calibration to absorb individual differences. For our earlier study, we developed a prototype that incorporates bed-leaving detection sensors using piezoelectric films and a machine-learning-based behavior recognition method using counter-propagation networks (CPNs). Our method learns topology and relations between input features and teaching signals. Nevertheless, CPNs have been insufficient to address individual differences in parameters such as weight and height used for bed-learning behavior recognition. For this study, we actualize automatic calibration of sensor signals for invariance relative to these body parameters. This paper presents two experimentally obtained results from our earlier study. They were obtained using low-accuracy sensor signals. For the preliminary experiment, we optimized the original sensor signals to approximate high-accuracy ideal sensor signals using generated filters. We used fitness to assess differences between the original signal patterns and ideal signal patterns. For application experiments, we used fitness calculated from the recognition accuracy obtained using CPNs. The experimentally obtained results reveal that our method improved the mean accuracies for three datasets.

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Section: Related Studiesmentioning

confidence: 99%

Automatic Calibration of Piezoelectric Bed-Leaving Sensor Signals Using Genetic Network Programming Algorithms

2021

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“…State of the art in this research can be seen that the integration of RL to Evolutionary Algorithms (EA), such as Genetic Algorithm (GA) [6][7][8], Genetic Programming (GP) [9][10][11] and Genetic Network Programming (GNP) [12] were studied in many researches [13][14][15][16][17], where the integration can improve the performance as shown in GNP with RL (GNP-RL) which was implemented to navigate the mobile robot [18]. EA has the evolving ability for capturing the environment using selection, crossover and mutation, while the integration of RL to EA improves the adaptability to the dynamic environments.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed navigation system of the mobile robot in this paper is based on GNP, where GNP has advantages [12] such as (1) re-usability of the nodes which make the structures more compact and (2) applicability to Partially Observable Markov Decision Problem (POMDP). Compared to the other methods, such as Evolutionary Neural Network (ENN) and GP, GNP has better performance [12,18]. Here, GNP with Two-Stage Reinforcement Learning (GNP-TSRL) to face inexperienced changes of the environments was studied.…”

Section: Introductionmentioning

confidence: 99%

Exploration of genetic network programming with two-stage reinforcement learning for mobile robot

Sendari¹,

Afandi²,

Zaeni³

et al. 2019

TELKOMNIKA

Self Cite

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This paper observes the exploration of Genetic Network Programming Two-Stage ReinforcementLearning for mobile robot navigation. The proposed method aims to observe its exploration when inexperienced environments used in the implementation. In order to deal with this situation, individuals are trained firstly in the training phase, that is, they learn the environment with ϵ-greedy policy and learning rate α parameters. Here, two cases are studied, i.e., case A for low exploration and case B for high exploration. In the implementation, the individuals implemented to get experience and learn a new environment on-line. Then, the performance of learning processes are observed due to the environmental changes.

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“…It is also a broad class of optimal control methods based on estimating value functions from experience, simulation, or search [12]. The RL has been used in many robotics domains, such as autonomous control [13,14], robot soccer [15], behavior making [16], multiagents coordination [17], path planning, etc. In mobile robot path planning, the RL is usually integrated with other approaches, such as neural network (NN), supervised learning (SL), and cerebellar model articulation controllers (CMACs).…”

Section: Introductionmentioning

confidence: 99%

Microassembly path planning using reinforcement learning for improving positioning accuracy of a 1 cm3 omni-directional mobile microrobot

Chen

2009

Appl Intell

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This paper introduces the path planning of a 1 cm 3 mobile microrobot that is designed for microassembly in a microfactory. Since the conventional path planning method can not achieve high microassembly positioning accuracy, a supervised learning assisted reinforcement learning (SL-RL) method has been developed. In this mixed learning method, the reinforcement learning (RL) is used to search a movement path in the normal learning area. But when the microrobot moves into the buffer area, the supervised learning (SL) is employed to prevent it from moving out of the boundary. The SL-RL uses a gradient descent algorithm based on uniform grid tile coding under SARSA(λ) to handle the large learning state space. In addition to the uniform grid tile model, two irregular tile models called an uneven grid tile model and a cobweb tile model are designed to partition the microrobot state space. The main conclusions demonstrated by simulations are as follows: First, the SL-RL method achieves higher positioning accuracy than the conventional path planning method; second, the SL-RL method achieves higher positioning accuracy and learning efficiency than the single RL method; and third, the irregular tile models show higher learning efficiency than the uniform tile model. The cobweb tile model performs especially well.

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Genetic Network Programming with Reinforcement Learning and Its Application to Making Mobile Robot Behavior

Cited by 17 publications

References 5 publications

Automatic Calibration of Piezoelectric Bed-Leaving Sensor Signals Using Genetic Network Programming Algorithms

Automatic Calibration of Piezoelectric Bed-Leaving Sensor Signals Using Genetic Network Programming Algorithms

Exploration of genetic network programming with two-stage reinforcement learning for mobile robot

Microassembly path planning using reinforcement learning for improving positioning accuracy of a 1 cm3 omni-directional mobile microrobot

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