scholarly journals ANALYSIS OF THE APPLICATION OF REINFORCEMENT LEARNING ALGORITHMS ON THE STARCRAFT II VIDEO GAME

2020 ◽  
Vol 11 (4) ◽  
Author(s):  
Leandro Vian ◽  
Marcelo De Gomensoro Malheiros
Keyword(s):  
Reinforcement Learning ◽  
Video Game ◽  
Cost Effective ◽  
Machine Learning Techniques ◽  
Specific Training ◽  
Board Games ◽  
Learning Techniques ◽  
Technical Issues ◽  
Strategy Game ◽  
Real Time Strategy Game

In recent years Machine Learning techniques have become the driving force behind the worldwide emergence of Artificial Intelligence, producing cost-effective and precise tools for pattern recognition and data analysis. A particular approach for the training of neural networks, Reinforcement Learning (RL), achieved prominence creating almost unbeatable artificial opponents in board games like Chess or Go, and also on video games. This paper gives an overview of Reinforcement Learning and tests this approach against a very popular real-time strategy game, Starcraft II. Our goal is to examine the tools and algorithms readily available for RL, also addressing different scenarios where a neural network can be linked to Starcraft II to learn by itself. This work describes both the technical issues involved and the preliminary results obtained by the application of two specific training strategies, A2C and DQN.

scholarly journals Developing an Open-Source Lightweight Game Engine with DNN Support

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1421
Author(s):  
Haechan Park ◽  
Nakhoon Baek
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Open Source ◽  
Programming Languages ◽  
Cost Effective ◽  
Machine Learning Techniques ◽  
Learning Technology ◽  
Game Engine ◽  
Learning Techniques ◽  
Technical Issues

With the growth of artificial intelligence and deep learning technology, we have many active research works to apply the related techniques in various fields. To test and apply the latest machine learning techniques in gaming, it will be very useful to have a light-weight game engine for quick prototyping. Our game engine is implemented in a cost-effective way, in comparison to well-known commercial proprietary game engines, by utilizing open source products. Due to its simple internal architecture, our game engine is especially beneficial for modifying and reviewing the new functions through quick and repetitive tests. In addition, the game engine has a DNN (deep neural network) module, with which the proposed game engine can apply deep learning techniques to the game features, through applying deep learning algorithms in real-time. Our DNN module uses a simple C++ function interface, rather than additional programming languages and/or scripts. This simplicity enables us to apply machine learning techniques more efficiently and casually to the game applications. We also found some technical issues during our development with open sources. These issues mostly occurred while integrating various open source products into a single game engine. We present details of these technical issues and our solutions.

scholarly journals Standing out in a networked communication context: Toward a network contingency model of public attention

2020 ◽  
pp. 146144482093944
Author(s):  
Aimei Yang ◽  
Adam J Saffer
Keyword(s):  
Social Media ◽  
Large Scale ◽  
Cost Effective ◽  
Machine Learning Techniques ◽  
Public Attention ◽  
Combine Data ◽  
Contingency Model ◽  
Learning Techniques ◽  
Community Strategy ◽  
Communication Context

Social media can offer strategic communicators cost-effective opportunities to reach millions of individuals. However, in practice it can be difficult to be heard in these crowded digital spaces. This study takes a strategic network perspective and draws from recent research in network science to propose the network contingency model of public attention. This model argues that in the networked social-mediated environment, an organization’s ability to attract public attention on social media is contingent on its ability to fit its network position with the network structure of the communication context. To test the model, we combine data mining, social network analysis, and machine-learning techniques to analyze a large-scale Twitter discussion network. The results of our analysis of Twitter discussion around the refugee crisis in 2016 suggest that in high core-periphery network contexts, “star” positions were most influential whereas in low core-periphery network contexts, a “community” strategy is crucial to attracting public attention.

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 Q-Learning based Routing Protocol to Enhance Network Lifetime in WSNs

2021 ◽  
Vol 13 (2) ◽  
pp. 57-80
Author(s):  
Arunita Kundaliya ◽  
D.K. Lobiyal
Keyword(s):  
Reinforcement Learning ◽  
Network Lifetime ◽  
Residual Energy ◽  
Efficient Solutions ◽  
Machine Learning Techniques ◽  
Q Learning ◽  
Learning Techniques ◽  
Aodv Protocol ◽  
Optimal Action ◽  
Additional Memory

In resource constraint Wireless Sensor Networks (WSNs), enhancement of network lifetime has been one of the significantly challenging issues for the researchers. Researchers have been exploiting machine learning techniques, in particular reinforcement learning, to achieve efficient solutions in the domain of WSN. The objective of this paper is to apply Q-learning, a reinforcement learning technique, to enhance the lifetime of the network, by developing distributed routing protocols. Q-learning is an attractive choice for routing due to its low computational requirements and additional memory demands. To facilitate an agent running at each node to take an optimal action, the approach considers node’s residual energy, hop length to sink and transmission power. The parameters, residual energy and hop length, are used to calculate the Q-value, which in turn is used to decide the optimal next-hop for routing. The proposed protocols’ performance is evaluated through NS3 simulations, and compared with AODV protocol in terms of network lifetime, throughput and end-to-end delay.

From information- to knowledge-management: the role of rule induction and neural net machine learning techniques in knowledge generation

1989 ◽  
Vol 15 (4-5) ◽  
pp. 299-304 ◽  
Author(s):  
Nigel Ford
Keyword(s):  
Machine Learning ◽  
Cost Effective ◽  
Rule Induction ◽  
Machine Learning Techniques ◽  
Knowledge Generation ◽  
Neural Net ◽  
Knowledge Resources ◽  
Learning Techniques ◽  
Knowledge Manager

Developments in artificial intelligence mean that it is now increasingly possible to store not only information but also knowledge as an exploitable resource. Insofar as he or she is concerned with creating, organizing and monitoring knowledge resources to support effective decision making within an organization, the information manager is developing the role of knowledge manager. As well as its organization and dissemina tion, the generation of storable knowledge is very much on the agenda of the knowledge manager. The extent to which com puters can help in the process of knowledge generation is central to his or her concerns. Machine learning techniques have been developed which are capable of giving us an increasing amount of help in this process. The contributions of rule induction and artificial neural net systems are discussed. It is likely that such tech niques will prove to be useful tools both for the information/knowledge manager requiring practical working systems enabling the cost-effective exploitation of knowledge resources, and for the information/knowledge scientist requir ing advances in our more fundamental theoretical knowledge of the nature of information and ways of processing it.

scholarly journals Reinforcement Learning-Enabled UAV Itinerary Planning for Remote Sensing Applications in Smart Farming

Telecom ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 255-270
Author(s):  
Saeid Pourroostaei Ardakani ◽  
Ali Cheshmehzangi
Keyword(s):  
Remote Sensing ◽  
Reinforcement Learning ◽  
Data Collection ◽  
Cost Effective ◽  
Environmental Data ◽  
Machine Learning Techniques ◽  
Q Learning ◽  
Sensing Applications ◽  
Learning Technique ◽  
Target Locations

UAV path planning for remote sensing aims to find the best-fitted routes to complete a data collection mission. UAVs plan the routes and move through them to remotely collect environmental data from particular target zones by using sensory devices such as cameras. Route planning may utilize machine learning techniques to autonomously find/select cost-effective and/or best-fitted routes and achieve optimized results including: minimized data collection delay, reduced UAV power consumption, decreased flight traversed distance and maximized number of collected data samples. This paper utilizes a reinforcement learning technique (location and energy-aware Q-learning) to plan UAV routes for remote sensing in smart farms. Through this, the UAV avoids heuristically or blindly moving throughout a farm, but this takes the benefits of environment exploration–exploitation to explore the farm and find the shortest and most cost-effective paths into target locations with interesting data samples to collect. According to the simulation results, utilizing the Q-learning technique increases data collection robustness and reduces UAV resource consumption (e.g., power), traversed paths, and remote sensing latency as compared to two well-known benchmarks, IEMF and TBID, especially if the target locations are dense and crowded in a farm.

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