scholarly journals Analyzing the Effects of Reinforcement Learning to Develop Humanoid Robots

2019 ◽  
Vol 8 (1) ◽  
pp. 55-66
Author(s):  
Naaima Suroor ◽  
Imran Hussain ◽  
Aqeel Khalique ◽  
Tabrej Ahamad Khan
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Problem Solving ◽  
Cognitive Abilities ◽  
Humanoid Robots ◽  
Human Intervention ◽  
Learning Techniques ◽  
Probable Impact ◽  
Learning Concept

Reinforcement learning is a flourishing machine learning concept that has greatly influenced how robots are designed and taught to solve problems without human intervention. Robotics is not an alien discipline anymore, and we have several great innovations in this field that promise to impact lives for the better. However, humanoid robots are still a baffling concept for scientists, although we have managed to develop a few great inventions which look, talk, work, and behave very similarly to humans. But, can these machines actually exhibit the cognitive abilities of judgment, problem-solving, and perception as well as humans? In this article, the authors analyzed the probable impact and aspects of robots and their potential to behave like humans in every possible way through reinforcement learning techniques. The paper also discusses the gap between 'natural' and 'artificial' knowledge.

Machine learning techniques based on security management in smart cities using robots

Work ◽  
2021 ◽  
pp. 1-12
Author(s):  
Zhang Mengqi ◽  
Wang Xi ◽  
V.E. Sathishkumar ◽  
V. Sivakumar
Keyword(s):  
Machine Learning ◽  
Information And Communication Technologies ◽  
Smart City ◽  
Smart Cities ◽  
Security Management ◽  
Machine Learning Techniques ◽  
Quality Of Services ◽  
Learning Techniques ◽  
Learning Concept

BACKGROUND: Nowadays, the growth of smart cities is enhanced gradually, which collects a lot of information and communication technologies that are used to maximize the quality of services. Even though the intelligent city concept provides a lot of valuable services, security management is still one of the major issues due to shared threats and activities. For overcoming the above problems, smart cities’ security factors should be analyzed continuously to eliminate the unwanted activities that used to enhance the quality of the services. OBJECTIVES: To address the discussed problem, active machine learning techniques are used to predict the quality of services in the smart city manages security-related issues. In this work, a deep reinforcement learning concept is used to learn the features of smart cities; the learning concept understands the entire activities of the smart city. During this energetic city, information is gathered with the help of security robots called cobalt robots. The smart cities related to new incoming features are examined through the use of a modular neural network. RESULTS: The system successfully predicts the unwanted activity in intelligent cities by dividing the collected data into a smaller subset, which reduces the complexity and improves the overall security management process. The efficiency of the system is evaluated using experimental analysis. CONCLUSION: This exploratory study is conducted on the 200 obstacles are placed in the smart city, and the introduced DRL with MDNN approach attains maximum results on security maintains.

scholarly journals Applying a Deep Q Network for OpenAIs Car Racing Game

2020 ◽  
Author(s):  
Ali Fakhry
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Transfer Learning ◽  
State Of The Art ◽  
Learning Techniques ◽  
Car Racing ◽  
Custom Made ◽  
Learning Technique ◽  
Reward Threshold

The applications of Deep Q-Networks are seen throughout the field of reinforcement learning, a large subsect of machine learning. Using a classic environment from OpenAI, CarRacing-v0, a 2D car racing environment, alongside a custom based modification of the environment, a DQN, Deep Q-Network, was created to solve both the classic and custom environments. The environments are tested using custom made CNN architectures and applying transfer learning from Resnet18. While DQNs were state of the art years ago, using it for CarRacing-v0 appears somewhat unappealing and not as effective as other reinforcement learning techniques. Overall, while the model did train and the agent learned various parts of the environment, attempting to reach the reward threshold for the environment with this reinforcement learning technique seems problematic and difficult as other techniques would be more useful.

Cloud Load Balancing and Reinforcement Learning

2018 ◽  
pp. 266-291
Author(s):  
Abdelghafour Harraz ◽  
Mostapha Zbakh
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Load Balancing ◽  
Decision Process ◽  
Cloud System ◽  
Human Intervention ◽  
Q Learning ◽  
State Action ◽  
Learning Techniques ◽  
Markov Decision

Artificial Intelligence allows to create engines that are able to explore, learn environments and therefore create policies that permit to control them in real time with no human intervention. It can be applied, through its Reinforcement Learning techniques component, using frameworks such as temporal differences, State-Action-Reward-State-Action (SARSA), Q Learning to name a few, to systems that are be perceived as a Markov Decision Process, this opens door in front of applying Reinforcement Learning to Cloud Load Balancing to be able to dispatch load dynamically to a given Cloud System. The authors will describe different techniques that can used to implement a Reinforcement Learning based engine in a cloud system.

Controlling a Simulated Robot Using Machine Learning Techniques

2010 ◽  
Author(s):  
Jonathan Becker ◽  
Aveek Purohit ◽  
Zheng Sun
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Linear Regression ◽  
Pid Controller ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Gaming Environment ◽  
Using Data

USARSim group at NIST developed a simulated robot that operated in the Unreal Tournament 3 (UT3) gaming environment. They used a software PID controller to control the robot in UT3 worlds. Unfortunately, the PID controller did not work well, so NIST asked us to develop a better controller using machine learning techniques. In the process, we characterized the software PID controller and the robot’s behavior in UT3 worlds. Using data collected from our simulations, we compared different machine learning techniques including linear regression and reinforcement learning (RL). Finally, we implemented a RL based controller in Matlab and ran it in the UT3 environment via a TCP/IP link between Matlab and UT3.

EFFECTS OF COMMUNICATION ON GROUP LEARNING RATES IN A MULTI-AGENT ENVIRONMENT

2003 ◽  
Vol 06 (03) ◽  
pp. 405-426 ◽  
Author(s):  
PAUL DARBYSHIRE
Keyword(s):  
Reinforcement Learning ◽  
Cognitive Abilities ◽  
Complex Adaptive System ◽  
Machine Learning Techniques ◽  
Simulation Techniques ◽  
Learning Rates ◽  
Learning Techniques ◽  
Complex Adaptive ◽  
Rate Of Learning ◽  
Multi Agent

Distillations utilize multi-agent based modeling and simulation techniques to study warfare as a complex adaptive system at the conceptual level. The focus is placed on the interactions between the agents to facilitate study of cause and effect between individual interactions and overall system behavior. Current distillations do not utilize machine-learning techniques to model the cognitive abilities of individual combatants but employ agent control paradigms to represent agents as highly instinctual entities. For a team of agents implementing a reinforcement-learning paradigm, the rate of learning is not sufficient for agents to adapt to this hostile environment. However, by allowing the agents to communicate their respective rewards for actions performed as the simulation progresses, the rate of learning can be increased sufficiently to significantly increase the teams chances of survival. This paper presents the results of trials to measure the success of a team-based approach to the reinforcement-learning problem in a distillation, using reward communication to increase learning rates.

scholarly journals The general design of the automation for multiple fields using reinforcement learning algorithm

2022 ◽  
Vol 25 (1) ◽  
pp. 481
Author(s):  
Vijaya Kumar Reddy Radha ◽  
Anantha N. Lakshmipathi ◽  
Ravi Kumar Tirandasu ◽  
Paruchuri Ravi Prakash
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Optimization Methods ◽  
Graph Representation ◽  
Machine Learning Technique ◽  
Learning Society ◽  
Learning Technique ◽  
Learning Concept

<p>Reinforcement learning is considered as a machine learning technique that is anxious with software agents should behave in particular environment. Reinforcement learning (RL) is a division of deep learning concept that assists you to make best use of some part of the collective return. In this paper evolving reinforcement learning algorithms shows possible to learn a fresh and understable concept by using a graph representation and applying optimization methods from the auto machine learning society. In this observe, we stand for the loss function, it is used to optimize an agent’s parameter in excess of its knowledge, as an imputational graph, and use traditional evolution to develop a population of the imputational graphs over a set of uncomplicated guidance environments. These outcomes in gradually better RL algorithms and the exposed algorithms simplify to more multifaceted environments, even though with visual annotations.</p>

scholarly journals Effective Use of Learning Knowledge by FEERL

2003 ◽  
Vol 7 (1) ◽  
pp. 6-9
Author(s):  
Yukinobu Hoshino ◽  
◽  
Katsuari Kamei
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Learning System ◽  
Trial And Error ◽  
Fuzzy Environment ◽  
Learning Techniques ◽  
Training Examples ◽  
Effective Use

The machine learning is proposed to learning techniques of spcialists. A machine has to learn techniques by trial and error when there are no training examples. Reinforcement learning is a powerful machine learning system, which is able to learn without giving training examples to a learning unit. But it is impossible for the reinforcement learning to support large environments because the number of if-then rules is a huge combination of a relationship between one environment and one action. We have proposed new reinforcement learning system for the large environment, Fuzzy Environment Evaluation Reinforcement Learning (FEERL). In this paper, we proposed to reuse of the acquired rules by FEERL.

Machine Learning and Deep Learning Technologies

2021 ◽  
Author(s):  
Yew Kee Wong
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Model Building ◽  
Learning Technologies ◽  
Decision Makers ◽  
Human Intervention ◽  
Learning Techniques ◽  
Tools And Techniques

In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets. Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Such minimal human intervention can be provided using machine learning, which is the application of advanced deep learning techniques on big data. This paper aims to analyse some of the different machine learning and deep learning algorithms and methods, aswell as the opportunities provided by the AI applications in various decision making domains.

scholarly journals Experiencing the Detection of Radicalized Criminals on Facebook Social Network and Data-related Issues

2020 ◽  
Author(s):  
Andrea Tundis ◽  
Leon Böck ◽  
Victoria Stanilescu ◽  
Max Mühlhäuser
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Problem Solving ◽  
Online Social Networks ◽  
Data Availability ◽  
Digital Tools ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Public Dataset

Online social networks (OSNs) represent powerful digital tools to communicate and quickly disseminate information in a non-official way. As they are freely accessible and easy to use, criminals abuse of them for achieving their purposes, for example, by spreading propaganda and radicalising people. Unfortunately, due to their vast usage, it is not always trivial to identify criminals using them unlawfully. Machine learning techniques have shown benefits in problem solving belonging to different application domains, when, due to the huge dimension in terms of data and variables to consider, it is not feasible their manual assessment. However, since the OSNs domain is relatively young, a variety of issues related to data availability makes it difficult to apply and immediately benefit from such techniques, in supporting the detection of criminals on OSNs. In this perspective, this paper wants to share the experience conducted in using a public dataset containing information related to criminals in order to both (i) extract specific features and to build a model for the detection of terrorists on Facebook social network, and (ii) to highlight the current limits. The research methodology as well as the gathered results are fully presented and then the data-related issues, emerged from this experience, are discussed. .

scholarly journals A Survey and Analysis of Multi-Label Learning Techniques for Data Streams

2020 ◽  
Vol 5 (7) ◽  
pp. 1014-1018
Author(s):  
S.K.Komagal Yallini ◽  
Dr. B. Mukunthan
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Data Streams ◽  
Data Stream ◽  
Predictive Performance ◽  
Class Label ◽  
The Past ◽  
Learning Techniques ◽  
The Future ◽  
Learning Concept

Multi-Label Learning (MLL) solves the challenge of characterizing every sample via a particular feature which relates to the group of labels at once. That is, a sample has manifold views where every view is symbolized through a Class Label (CL). In the past decades, significant number of researches has been prepared towards this promising machine learning concept. Such researches on MLL have been motivated on a pre-determined group of CLs. In most of the appliances, the configuration is dynamic and novel views might appear in a Data Stream (DS). In this scenario, a MLL technique should able to identify and categorize the features with evolving fresh labels for maintaining a better predictive performance. For this purpose, several MLL techniques were introduced in the earlier decades. This article aims to present a survey on this field with consequence on conventional MLL techniques. Initially, various MLL techniques proposed by many researchers are studied. Then, a comparative analysis is carried out in terms of merits and demerits of those techniques to conclude the survey and recommend the future enhancements on MLL techniques.

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