scholarly journals A Novel Behavioral Strategy for RoboCode Platform Based on Deep Q-Learning

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Hakan Kayakoku ◽  
Mehmet Serdar Guzel ◽  
Erkan Bostanci ◽  
Ihsan Tolga Medeni ◽  
Deepti Mishra
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Behavioral Strategy ◽  
Simulation Environment ◽  
Space Problem ◽  
Cluttered Environments ◽  
New Model ◽  
Q Learning ◽  
Simulation Performance ◽  
Proposed Model

This paper addresses a new machine learning-based behavioral strategy using the deep Q-learning algorithm for the RoboCode simulation platform. According to this strategy, a new model is proposed for the RoboCode platform, providing an environment for simulated robots that can be programmed to battle against other robots. Compared to Atari Games, RoboCode has a fairly wide set of actions and situations. Due to the challenges of training a CNN model for such a continuous action space problem, the inputs obtained from the simulation environment were generated dynamically, and the proposed model was trained by using these inputs. The trained model battled against the predefined rival robots of the environment (standard robots) by cumulatively benefiting from the experience of these robots. The comparison between the proposed model and standard robots of RoboCode Platform was statistically verified. Finally, the performance of the proposed model was compared with machine learning based-customized robots (community robots). Experimental results reveal that the proposed model is mostly superior to community robots. Therefore, the deep Q-learning-based model has proven to be successful in such a complex simulation environment. It should also be noted that this new model facilitates simulation performance in adaptive and partially cluttered environments.

Detection and prediction of pluvial flood using machine learning techniques

2021 ◽  
Vol 27 (2) ◽  
Author(s):  
K.A. Oladapo ◽  
F.Y. Ayankoya ◽  
F.A. Adekunle ◽  
S.A. Idowu
Keyword(s):  
Machine Learning ◽  
Flood Risk ◽  
Scientific Method ◽  
Learning Algorithm ◽  
Fuzzy Rule ◽  
Machine Learning Techniques ◽  
Emergency Situations ◽  
Learning Techniques ◽  
Proposed Model ◽  
Flood Detection

The periodical occurrence of emergency situations represents an important issue for mankind. Over the years, the world at large has experienced multiple misadventures both natural and man-made. A recent report showed that flood have affected more individuals than any other category of disaster in the 21st century with the highest percentage of 43% of all disaster events in 2019 and Africa been the second vulnerable continent after Asia. Handling flood risk with the intention of safety and comfort of the citizens as well as saving their environment is one of the major responsibilities of the leadership in each country especially in flood prone areas. Machine learning predictive analytic applications can improve the risk management. So, it is highly important to devise a scientific method for flood risk reduction since it cannot be eradicated. The paper proposes a pluvial flood detection and prediction system based on machine learning techniques. The proposed model will employ a fuzzy rule-based classification to appraise the performance of the machine learning algorithm on pluvial flood conditioning variables.

scholarly journals Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 932
Author(s):  
Ziqiu Kang ◽  
Cagatay Catal ◽  
Bedir Tekinerdogan
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Continuous Production ◽  
Case Study Research ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Production Lines ◽  
Important Concern ◽  
Proposed Model ◽  
Useful Life

Predictive maintenance of production lines is important to early detect possible defects and thus identify and apply the required maintenance activities to avoid possible breakdowns. An important concern in predictive maintenance is the prediction of remaining useful life (RUL), which is an estimate of the number of remaining years that a component in a production line is estimated to be able to function in accordance with its intended purpose before warranting replacement. In this study, we propose a novel machine learning-based approach for automating the prediction of the failure of equipment in continuous production lines. The proposed model applies normalization and principle component analysis during the pre-processing stage, utilizes interpolation, uses grid search for parameter optimization, and is built with multilayer perceptron neural network (MLP) machine learning algorithm. We have evaluated the approach using a case study research to predict the RUL of engines on NASA turbo engine datasets. Experimental results demonstrate that the performance of our proposed model is effective in predicting the RUL of turbo engines and likewise substantially enhances predictive maintenance results.

scholarly journals Machine Learning MOSA Monitoring System

2021 ◽  
Vol 20 (4) ◽  
pp. 203-208
Author(s):  
Aditya G. Manjunath ◽  
Sabahudin Vrtagić ◽  
Fatih Dogan ◽  
Milan Dordevic ◽  
Mileta Zarkovic ◽  
...  
Keyword(s):  
Machine Learning ◽  
Condition Monitoring ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Operating Voltage ◽  
Machine Learning Algorithm ◽  
Applied Learning ◽  
Proposed Model ◽  
Simulated System ◽  
Monitoring And Diagnostics

This research paper deals with the problem of Metal-Oxide Surge Arrester (MOSA) condition monitoring and a new methodology in surge arrester monitoring and diagnostics is presented. A machine learning algorithm (back propagation regression) is used to estimate the non-linearity coefficient of the surge arrester, based on operating voltage and leakage current of the arrester. Using a simulated system, this research investigates the possibility of application and efficiency of machine learning. It is shown that the applied learning algorithm results are competitive with the model results parameters calculated as R2 = 0.999 and mean absolute real error computed as 0.005 which has shown that the proposed model can be used for MOSA monitoring and diagnostic purposes.

Used Cars Price Prediction using Machine Learning with Optimal Features

2021 ◽  
Vol 4 (2) ◽  
pp. 113-119
Author(s):  
Muhammad Asghar ◽  
Khalid Mehmood ◽  
Samina Yasin ◽  
Zimal Mehboob Khan
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Model Performance ◽  
Market Value ◽  
Statistical Test ◽  
Difficult Decision ◽  
Price Prediction ◽  
Proposed Model ◽  
Design Value ◽  
Used Cars

We all are needed the personal vehicle that could help us to travel from home to office and travel to vocations means we need the personal vehicle for traveling for this we purchase the new vehicle or used vehicle this is some time take so much to take decision for purchasing the new one and most difficult decision is to take how to sale the old one that is already we have keep using if we sale and what is best price we can get or gives us more benefits. More over the purchasing power of the customers is low due to the prices of the new cars. There are different methods to predict the price of the car according to market value. Our proposed method helps the both the purchase and seller for to purchase and sale their vehicle and they can predict the best for their vehicle and make their decision good for personal and business. Our proposed model performance shows that the proposed study is productive and efficient. In the proposed study the machine learning algorithm Regression helps in the outperform. Here we use the Statistical test to get the design value of P and get the optimal features and using the linear regression. First, we find the RFE and then apply the statistical test for VIF for the OLS Regression. Prediction results shows the study is efficient and effective.

Ethology-Based Approximate Adaptive Learning

2012 ◽  
pp. 1856-1878
Author(s):  
James F. Peters ◽  
Shabnam Shahfar
Keyword(s):  
Machine Learning ◽  
Learning Theory ◽  
Adaptive Learning ◽  
Learning Algorithm ◽  
Near Sets ◽  
Practical Applications ◽  
Q Learning ◽  
Changing Environments ◽  
Complete Framework ◽  
Observed Behavior

The problem considered in this chapter is how to use the observed behavior of organisms as a basis for machine learning. The proposed approach for machine learning combines near sets and ethology. It leads to novel forms of Q-learning algorithm that have practical applications in the controlling the behavior of machines, which learn to adapt to changing environments. Both traditional and new forms of adaptive learning theory and applications are considered in this chapter. A complete framework for an ethology-based approximate adaptive learning is established by using near sets.

Ethology-Based Approximate Adaptive Learning

2011 ◽  
pp. 469-494
Author(s):  
James F. Peters ◽  
Shabnam Shahfar
Keyword(s):  
Machine Learning ◽  
Learning Theory ◽  
Adaptive Learning ◽  
Learning Algorithm ◽  
Near Sets ◽  
Practical Applications ◽  
Q Learning ◽  
Changing Environments ◽  
Complete Framework ◽  
Observed Behavior

The problem considered in this chapter is how to use the observed behavior of organisms as a basis for machine learning. The proposed approach for machine learning combines near sets and ethology. It leads to novel forms of Q-learning algorithm that have practical applications in the controlling the behavior of machines, which learn to adapt to changing environments. Both traditional and new forms of adaptive learning theory and applications are considered in this chapter. A complete framework for an ethology-based approximate adaptive learning is established by using near sets.

scholarly journals Adoption of Machine Learning Algorithm-Based Intelligent Basketball Training Robot in Athlete Injury Prevention

2021 ◽  
Vol 14 ◽  
Author(s):  
Teng Xu ◽  
Lijun Tang
Keyword(s):  
Machine Learning ◽  
Path Planning ◽  
Obstacle Avoidance ◽  
Fuzzy Controller ◽  
Sports Injuries ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Motion Trajectory ◽  
Q Learning ◽  
Original Algorithm

In order to effectively prevent sports injuries caused by collisions in basketball training, realize efficient shooting, and reduce collisions, the machine learning algorithm was applied to intelligent robot for path planning in this study. First of all, combined with the basketball motion trajectory model, the sport recognition in basketball training was analyzed. Second, the mathematical model of the basketball motion trajectory of the shooting motion was established, and the factors affecting the shooting were analyzed. Thirdly, on this basis, the machine learning-based improved Q-Learning algorithm was proposed, the path planning of the moving robot was realized, and the obstacle avoidance behavior was accomplished effectively. In the path planning, the principle of fuzzy controller was applied, and the obstacle ultrasonic signals acquired around the robot were taken as input to effectively avoid obstacles. Finally, the robot was able to approach the target point while avoiding obstacles. The results of simulation experiment show that the obstacle avoidance path obtained by the improved Q-Learning algorithm is flatter, indicating that the algorithm is more suitable for the obstacle avoidance of the robot. Besides, it only takes about 250 s for the robot to find the obstacle avoidance path to the target state for the first time, which is far lower than the 700 s of the previous original algorithm. As a result, the fuzzy controller applied to the basketball robot can effectively avoid the obstacles in the robot movement process, and the motion trajectory curve obtained is relatively smooth. Therefore, the proposed machine learning algorithm has favorable obstacle avoidance effect when it is applied to path planning in basketball training, and can effectively prevent sports injuries in basketball activities.

scholarly journals Learning Obstacle Avoidance with an Operant Behavior Model

2004 ◽  
Vol 10 (1) ◽  
pp. 65-81 ◽  
Author(s):  
D. A. Gutnisky ◽  
B. S. Zanutto
Keyword(s):  
Artificial Intelligence ◽  
Mobile Agents ◽  
Learning Algorithm ◽  
Robot Learning ◽  
Operant Learning ◽  
Avoidance Task ◽  
Q Learning ◽  
Proposed Model ◽  
Learning Research ◽  
High Flexibility

Artificial intelligence researchers have been attracted by the idea of having robots learn how to accomplish a task, rather than being told explicitly. Reinforcement learning has been proposed as an appealing framework to be used in controlling mobile agents. Robot learning research, as well as research in biological systems, face many similar problems in order to display high flexibility in performing a variety of tasks. In this work, the controlling of a vehicle in an avoidance task by a previously developed operant learning model (a form of animal learning) is studied. An environment in which a mobile robot with proximity sensors has to minimize the punishment for colliding against obstacles is simulated. The results were compared with the Q-Learning algorithm, and the proposed model had better performance. In this way a new artificial intelligence agent inspired by neurobiology, psychology, and ethology research is proposed.

scholarly journals HOW CAN MACHINE LEARNING HELP THE AUTHORITIES?

2021 ◽  
Vol 2020 (1) ◽  
pp. 370-376
Author(s):  
Samuel Ady Sanjaya ◽  
Suryo Adi Rakhmawan
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Action Planning ◽  
Face Mask ◽  
Public Place ◽  
Communication Tool ◽  
Health Crisis ◽  
Surveillance Camera ◽  
Proposed Model ◽  
The Face

Corona Virus Desease (COVID-19) pandemic is causing health crisis in every region in the world, especially in Indonesia. One of the effective methods against the virus is wearing face mask in public place as the regulation made by the authorities. This paper introduces face mask detection that can be used by the authorities to make mitigation, evaluation, prevention, and action planning against COVID-19. On the other hand, this solution can be used as communication tool to evaluate people’s habit on wearing face mask. The face mask recognition in this study is developed with machine learning algorithm through the image classification method: MobileNetv2. The proposed model can be integrated with surveillance camera to impede the Covid-19 transmission by allowing the detection of people who are not wearing face mask. After the training, validation, and testing phase, the model can provide the percentage of people using face mask in some cities with high accuracy. The data produced also have a strong correlation to the vigilance index of COVID-19.

scholarly journals Application of Deep Q-learning and double Deep Q-learning algorithms to the task of control an inverted pendulum

2020 ◽  
pp. 7-25
Author(s):  
Alla Evseenko ◽  
◽  
Dmitrii Romannikov ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reinforcement Learning ◽  
Inverted Pendulum ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Training Time ◽  
Q Learning ◽  
Research Areas ◽  
Wide Range

Today, such a branch of science as «artificial intelligence» is booming in the world. Systems built on the basis of artificial intelligence methods have the ability to perform functions that are traditionally considered the prerogative of man. Artificial intelligence has a wide range of research areas. One such area is machine learning. This article discusses the algorithms of one of the approaches of machine learning – reinforcement learning (RL), according to which a lot of research and development has been carried out over the past seven years. Development and research on this approach is mainly carried out to solve problems in Atari 2600 games or in other similar ones. In this article, reinforcement training will be applied to one of the dynamic objects – an inverted pendulum. As a model of this object, we consider a model of an inverted pendulum on a cart taken from the Gym library, which contains many models that are used to test and analyze reinforcement learning algorithms. The article describes the implementation and study of two algorithms from this approach, Deep Q-learning and Double Deep Q-learning. As a result, training, testing and training time graphs for each algorithm are presented, on the basis of which it is concluded that it is desirable to use the Double Deep Q-learning algorithm, because the training time is approximately 2 minutes and provides the best control for the model of an inverted pendulum on a cart.

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