scholarly journals Optimal Skipping Rates: Training Agents with Fine-Grained Control Using Deep Reinforcement Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Adil Khan ◽  
Jiang Feng ◽  
Shaohui Liu ◽  
Muhammad Zubair Asghar
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Computer Games ◽  
Learning Experience ◽  
Research Work ◽  
Real Life ◽  
Basic Scenario ◽  
Final Performance ◽  
Game Ai ◽  
Research Platform

These days game AI is one of the focused and active research areas in artificial intelligence because computer games are the best test-beds for testing theoretical ideas in AI before practically applying them in real life world. Similarly, ViZDoom is a game artificial intelligence research platform based on Doom used for visual deep reinforcement learning in 3D game environments such as first-person shooters (FPS). While training, the speed of the learning agent greatly depends on the number of frames the agent is permitted to skip. In this paper, how the frame skipping rate influences the agent’s learning and final performance is proposed, particularly using deep Q-learning, experience replay memory, and the ViZDoom Game AI research platform. The agent is trained and tested on Doom’s basic scenario(s) where the results are compared and found to be 10% better compared to the existing state-of-the-art research work on Doom-based agents. The experiments show that the profitable and optimal frame skipping rate falls in the range of 3 to 11 that provides the best balance between the learning speed and the final performance of the agent which exhibits human-like behavior and outperforms an average human player and inbuilt game agents.

Playing Doom with Anticipator-A3C Based Agents Using Deep Reinforcement Learning and the ViZDoom Game-AI Research Platform

2021 ◽  
pp. 503-562
Author(s):  
Adil Khan ◽  
Muhammad Naeem ◽  
Asad Masood Khattak ◽  
Muhammad Zubair Asghar ◽  
Abdul Haseeb Malik
Keyword(s):  
Reinforcement Learning ◽  
Game Ai ◽  
Research Platform

scholarly journals WAYS TO USE NEURAL NETWORKS AND MACHINE LEARNING IN COMPUTER GAMES

2021 ◽  
Vol 295 (2) ◽  
pp. 97-100
Author(s):  
K. Seniva ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Computer Games ◽  
Real Life ◽  
Gaming Industry ◽  
Game Development ◽  
Machine Learning Methods ◽  
Long Time ◽  
And Behavior

This article discusses the main ways of using neural networks and machine learning methods of various types in computer games. Machine learning and neural networks are hot topics in many technology fields. One of them is the creation of computer games, where new tools are used to make games more interesting. Remastered and modified games with neural networks have become a new trend. One of the most popular ways to implement artificial intelligence is neural networks. They are used in everything from medicine to the entertainment industry. But one of the most promising areas for their development is games. The game world is an ideal platform for testing artificial intelligence without the danger of harming nature or people. Making bots more complex is just a small part of what neural networks can do. They are also actively used in game development, and in some areas they already make people feel uncomfortable. Research is ongoing on color and light correction, real-time character animation and behavior control. The main types of neural networks that can learn such functions are considered. Neural networks learn (and self-learn) very quickly. The more primitive the task, the faster the person will become unnecessary. This is already noticeable in the gaming industry, but will soon spread to other areas of life, because games are just a convenient platform for experimenting with artificial intelligence before its implementation in real life. The main problem faced by scientists is that it is difficult for neural networks to copy the mechanics of the game. There are some achievements in this direction, but research continues. Therefore, in the future, real specialists will be required for the development of games for a long time, although AI is already coping with some tasks.

Teaching Software Engineering Through a Collaborative Game

2017 ◽  
pp. 874-895 ◽  
Author(s):  
Elizabeth Suescún Monsalve ◽  
Allan Ximenes Pereira ◽  
Vera Maria B. Werneck
Keyword(s):  
Software Engineering ◽  
Computer Games ◽  
Teaching And Learning ◽  
Software Engineer ◽  
Learning Experience ◽  
Real Life ◽  
Teaching Experience ◽  
Software Engineering Process ◽  
Correct Technique ◽  
Games And Simulations

This chapter addresses the application of computer games and simulations in order to explore reality in many educational areas. The Games-Based Learning (GBL) can improve the teaching and learning experience by training future professionals in real life scenarios and activities that enable them to apply problem-solving strategies by putting into use the correct technique stemming from their own skills. For that reason, GBL has been used in software engineering teaching. At Pontifical Catholic University of Rio de Janeiro, the authors have developed SimulES-W (Simulation in Software Engineering), a tool for teaching software engineering. SimulES-W is a collaborative software board game that simulates a software engineering process in which the player performs different roles such as software engineer, technical coordinator, project manager, and quality controller. The players can deal with budget, software engineer employment and dismissal, and construction of different software artifacts. The objective of this chapter is to describe the approach to teaching software engineering using SimulES-W and demonstrate how pedagogical methodology is applied in this teaching approach to improve software engineering education. The teaching experience and future improvements are also discussed.

scholarly journals MAPF Scenario: Software for Evaluating MAPF Plans on Real Robots

2020 ◽  
Vol 34 (09) ◽  
pp. 13602-13603
Author(s):  
Roman Barták ◽  
Jiří Švancara ◽  
Ivan Krasičenko
Keyword(s):  
Artificial Intelligence ◽  
Computer Games ◽  
Research Community ◽  
Academic Community ◽  
Practical Applications ◽  
Control Programs ◽  
Abstract Version ◽  
Research Platform ◽  
Multi Agent ◽  
And Robotics

Multi-Agent Path Finding (MAPF) deals with finding collision free paths for a set of agents (robots) moving on a graph. The interest in MAPF in the research community started to increase recently partly due to practical applications in areas such as warehousing and computer games. However, the academic community focuses mostly on solving the abstract version of the problem (moving of agents on the graph) with only a few results on real robots. The presented software MAPF Scenario provides a tool for specifying MAPF problems on grid maps, solving the problems using various abstractions (for example, assuming rotation actions or not), simulating execution of plans, and translating the abstract plans to control programs for small robots Ozobots. The tool is intended as a research platform for evaluating abstract MAPF plans on real robots and as an educational and demonstration tool bridging the areas of artificial intelligence and robotics.

scholarly journals Generating Behavior-Diverse Game AIs with Evolutionary Multi-Objective Deep Reinforcement Learning

2020 ◽  
Author(s):  
Ruimin Shen ◽  
Yan Zheng ◽  
Jianye Hao ◽  
Zhaopeng Meng ◽  
Yingfeng Chen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Domain Knowledge ◽  
Behavioral Characteristic ◽  
Multi Objective ◽  
Behavioral Diversity ◽  
Game Ai ◽  
Game Industry ◽  
Characteristic Style ◽  
New Framework

Generating diverse behaviors for game artificial intelligence (Game AI) has been long recognized as a challenging task in the game industry. Designing a Game AI with a satisfying behavioral characteristic (style) heavily depends on the domain knowledge and is hard to achieve manually. Deep reinforcement learning sheds light on advancing the automatic Game AI design. However, most of them focus on creating a superhuman Game AI, ignoring the importance of behavioral diversity in games. To bridge the gap, we introduce a new framework, named EMOGI, which can automatically generate desirable styles with almost no domain knowledge. More importantly, EMOGI succeeds in creating a range of diverse styles, providing behavior-diverse Game AIs. Evaluations on the Atari and real commercial games indicate that, compared to existing algorithms, EMOGI performs better in generating diverse behaviors and significantly improves the efficiency of Game AI design.

Teaching Software Engineering through a Collaborative Game

2014 ◽  
pp. 310-331
Author(s):  
Elizabeth Suescún Monsalve ◽  
Allan Ximenes Pereira ◽  
Vera Maria B. Werneck
Keyword(s):  
Software Engineering ◽  
Computer Games ◽  
Teaching And Learning ◽  
Software Engineer ◽  
Learning Experience ◽  
Real Life ◽  
Teaching Experience ◽  
Software Engineering Process ◽  
Correct Technique ◽  
Games And Simulations

This chapter addresses the application of computer games and simulations in order to explore reality in many educational areas. The Games-Based Learning (GBL) can improve the teaching and learning experience by training future professionals in real life scenarios and activities that enable them to apply problem-solving strategies by putting into use the correct technique stemming from their own skills. For that reason, GBL has been used in software engineering teaching. At Pontifical Catholic University of Rio de Janeiro, the authors have developed SimulES-W (Simulation in Software Engineering), a tool for teaching software engineering. SimulES-W is a collaborative software board game that simulates a software engineering process in which the player performs different roles such as software engineer, technical coordinator, project manager, and quality controller. The players can deal with budget, software engineer employment and dismissal, and construction of different software artifacts. The objective of this chapter is to describe the approach to teaching software engineering using SimulES-W and demonstrate how pedagogical methodology is applied in this teaching approach to improve software engineering education. The teaching experience and future improvements are also discussed.

CONTENT OF TEACHING THE ELEMENTS OF ARTIFICIAL INTELLIGENCE IN A SCHOOL INFORMATICS COURSE

2020 ◽  
pp. 3-10
Author(s):  
I. V. Levchenko
Keyword(s):  
Artificial Intelligence ◽  
General Education ◽  
Extracurricular Activities ◽  
Information Technologies ◽  
Learning Experience ◽  
9Th Grade ◽  
Modern Information ◽  
Further Development ◽  
Form Variant ◽  
Theoretical Foundations

The article considers the feasibility of integrating artificial intelligence technologies into school education and identifies a problem in identifying didactic elements in the field of artificial intelligence, which must be mastered in a school informatics course. The purpose of the article is to propose variant of the content of teaching the elements of artificial intelligence for the general education of schoolchildren as part of the curricular and extracurricular activities in informatics. An analysis of the psychological, pedagogical and scientific-methodical literature in the field of artificial intelligence made it possible to identify the appropriateness of teaching schoolchildren the elements of artificial intelligence in the framework of a comprehensive informatics course, as the theoretical foundations of modern information technologies. Summarizing and systematizing the learning experience of schoolchildren in the field of artificial intelligence made it possible to form variant of the content of teaching the elements of artificial intelligence, which can be implemented in a compulsory informatics course for 9th grade, as well as in elective classes. The results of the study are the theoretical basis for the further development of the components of the methodological system of teaching the elements of artificial intelligence in a school informatics course. The research materials may be useful to specialists in the field of teaching informatics and to informatics teachers.

scholarly journals Dealing with multiple experts and non-stationarity in inverse reinforcement learning: an application to real-life problems

2021 ◽  
Author(s):  
Amarildo Likmeta ◽  
Alberto Maria Metelli ◽  
Giorgia Ramponi ◽  
Andrea Tirinzoni ◽  
Matteo Giuliani ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
Real Life ◽  
User Preferences ◽  
Inverse Reinforcement Learning ◽  
Water Release ◽  
Reward Function ◽  
Model Free ◽  
Conflicting Objectives ◽  
Multiple Experts

AbstractIn real-world applications, inferring the intentions of expert agents (e.g., human operators) can be fundamental to understand how possibly conflicting objectives are managed, helping to interpret the demonstrated behavior. In this paper, we discuss how inverse reinforcement learning (IRL) can be employed to retrieve the reward function implicitly optimized by expert agents acting in real applications. Scaling IRL to real-world cases has proved challenging as typically only a fixed dataset of demonstrations is available and further interactions with the environment are not allowed. For this reason, we resort to a class of truly batch model-free IRL algorithms and we present three application scenarios: (1) the high-level decision-making problem in the highway driving scenario, and (2) inferring the user preferences in a social network (Twitter), and (3) the management of the water release in the Como Lake. For each of these scenarios, we provide formalization, experiments and a discussion to interpret the obtained results.

scholarly journals Drone Deep Reinforcement Learning: A Review

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 999
Author(s):  
Ahmad Taher Azar ◽  
Anis Koubaa ◽  
Nada Ali Mohamed ◽  
Habiba A. Ibrahim ◽  
Zahra Fathy Ibrahim ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
Real Life ◽  
Environment Monitoring ◽  
Simulated Environments ◽  
Infrastructure Inspection ◽  
Remote Sensing Mapping ◽  
And Control ◽  
The Military ◽  
Uav Navigation

Unmanned Aerial Vehicles (UAVs) are increasingly being used in many challenging and diversified applications. These applications belong to the civilian and the military fields. To name a few; infrastructure inspection, traffic patrolling, remote sensing, mapping, surveillance, rescuing humans and animals, environment monitoring, and Intelligence, Surveillance, Target Acquisition, and Reconnaissance (ISTAR) operations. However, the use of UAVs in these applications needs a substantial level of autonomy. In other words, UAVs should have the ability to accomplish planned missions in unexpected situations without requiring human intervention. To ensure this level of autonomy, many artificial intelligence algorithms were designed. These algorithms targeted the guidance, navigation, and control (GNC) of UAVs. In this paper, we described the state of the art of one subset of these algorithms: the deep reinforcement learning (DRL) techniques. We made a detailed description of them, and we deduced the current limitations in this area. We noted that most of these DRL methods were designed to ensure stable and smooth UAV navigation by training computer-simulated environments. We realized that further research efforts are needed to address the challenges that restrain their deployment in real-life scenarios.

Reinforcement Learning for Cloud Computing Digital Library

2014 ◽  
Vol 571-572 ◽  
pp. 105-108
Author(s):  
Lin Xu
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Reinforcement Learning ◽  
Digital Library ◽  
Learning Algorithms ◽  
Experimental Results ◽  
Current Status ◽  
Self Learning ◽  
New Framework

This paper proposes a new framework of combining reinforcement learning with cloud computing digital library. Unified self-learning algorithms, which includes reinforcement learning, artificial intelligence and etc, have led to many essential advances. Given the current status of highly-available models, analysts urgently desire the deployment of write-ahead logging. In this paper we examine how DNS can be applied to the investigation of superblocks, and introduce the reinforcement learning to improve the quality of current cloud computing digital library. The experimental results show that the method works more efficiency.

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