Genetic Network Programming with Reinforcement Learning Using Sarsa Algorithm

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

doi:10.1109/cec.2006.1688346

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Since GNP represents its solutions using graph structures, which contributes to creating quite compact programs and implicitly memorizing past action sequences in the network flows, it has been clarified that GNP is an effective method mainly for dynamic problems such as stock trading systems. Moreover, in our former research, GNP was successfully applied to stock trading model by the combination with reinforcement learning (GNP-RL) [5]- [7], and its applicability and efficiency has been confirmed. As we know, original GNP is based on evolution only, therefore the programs are evolved mainly after task execution or enough trial, i.e., offline learning.…”

Section: Introductionmentioning

confidence: 99%

Real Time Updating Genetic Network Programming for adapting to the change of stock prices

Chen

Mabu

Shimada

et al. 2008

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

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The key in stock trading model is to take the right actions for trading at the right time, primarily based on accurate forecast of future stock trends. Since an effective trading with given information of stock prices needs an intelligent strategy for the decision making, we applied Genetic Network Programming (GNP) to creat a stock trading model. In this paper, we present a new method called Real Time Updating Genetic Network Programming (RTU-GNP) for adapting to the change of stock prices. There are two important points in this paper: First, the RTU-GNP method makes a stock trading decision considering both the recommendable information of technical indices and the change of stock prices according to the real time updating. Second, we combine RTU-GNP with a reinforcement learning algorithm to creat the programs efficiently. The experimental results on the Japanese stock market show that the trading model with the proposed RTU-GNP method outperforms other models without time updating method. It yielded significantly higher profits than the traditional trading model without time uptating. We also compare the experimental results using the proposed method with Buy&Hold method to confirm its effectiveness, and it is clarified that the proposed trading model can obtain much higher profits than Buy&Hold method.

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Section: Introductionmentioning

confidence: 99%

Real Time Updating Genetic Network Programming for adapting to the change of stock prices

Chen

Mabu

Shimada

et al. 2008

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

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“…GNP-RL [29][30][31][32][33][34] is an extension of GNP [35] and it is efficiently combined evolution and learning. Evolutionary computation generally has an advantage in diversified search ability, while reinforcement learning has an advantage in intensified search ability and online learning.…”

Section: Introductionmentioning

confidence: 99%

Collision Prediction based Genetic Network Programming-Reinforcement Learning for Mobile Robot Navigation in Unknown Dynamic Environments

Findi¹,

Marhaban²,

Kamil³

et al. 2017

Journal of Electrical Engineering and Technology

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-The problem of determining a smooth and collision-free path with maximum possible speed for a Mobile Robot (MR) which is chasing a moving target in a dynamic environment is addressed in this paper. Genetic Network Programming with Reinforcement Learning (GNP-RL) has several important features over other evolutionary algorithms such as it combines offline and online learning on the one hand, and it combines diversified and intensified search on the other hand, but it was used in solving the problem of MR navigation in static environment only. This paper presents GNP-RL based on predicting collision positions as a first attempt to apply it for MR navigation in dynamic environment. The combination between features of the proposed collision prediction and that of GNP-RL provides safe navigation (effective obstacle avoidance) in dynamic environment, smooth movement, and reducing the obstacle avoidance latency time. Simulation in dynamic environment is used to evaluate the performance of collision prediction based GNP-RL compared with that of two state-of-the art navigation approaches, namely, Q-Learning (QL) and Artificial Potential Field (APF). The simulation results show that the proposed GNP-RL outperforms both QL and APF in terms of smooth movement and safer navigation. In addition, it outperforms APF in terms of preserving maximum possible speed during obstacle avoidance.

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Graph structure optimization of Genetic Network Programming with ant colony mechanism in deterministic and stochastic environments

Roshanzamir

Palhang

Mirzaei

2019

Swarm and Evolutionary Computation

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Genetic Network Programming with Reinforcement Learning Using Sarsa Algorithm

Cited by 3 publications

References 6 publications

Real Time Updating Genetic Network Programming for adapting to the change of stock prices

Real Time Updating Genetic Network Programming for adapting to the change of stock prices

Collision Prediction based Genetic Network Programming-Reinforcement Learning for Mobile Robot Navigation in Unknown Dynamic Environments

Graph structure optimization of Genetic Network Programming with ant colony mechanism in deterministic and stochastic environments

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