The research problem of this article is the Game AI agent of StarCraft II based on Deep Reinforcement Learning (DRL). StarCraft II is viewed as the most challenging Real-time Strategy (RTS) game for now, and it is also the most popular game where researchers are developing and improving AI agents. Building AI agents of StarCraft II can help researchers on machine learning figure out the weakness of DRL and improve this series of algorithms. In 2018, DeepMind and Blizzard developed the StarCraft II Learning Environment (PySC2) to enable researchers to promote the development of AI agents. DeepMind started to develop a new project called AlphaStar after AlphaGo based on DRL, while several laboratories also published articles about the AI agents of StarCraft II. Most of them are researching on the AI agents of Terran and Zerg, which are two of three races in StarCraft II. AI agents show high-level performance compared with most StarCraft II players. However, the performance is far from defeating E-sport players because Game AI for StarCraft II has large observation space and large action space. However, there is no publication on Protoss, which is the remaining and most complicated race to deal with (larger action space, larger observation space) for AI agents due to its characteristics. Thus, in this paper, the research question is whether the AI agent of Protoss, which is developed by the model based on DRL, for a full-length game on a particular map can defeat the high-level built-in cheating AI. The population of this research design is the StarCraft II AI agents that researchers built based on their DRL models, while the sample is the Protoss AI agent in this paper. The raw data is from the game matches between the Protoss AI agent and built-in AI agents. PySC2 can capture features and numerical variables in each match to obtain the training data. The expected outcome is the model based on DRL, which can train a Protoss AI agent to defeat high-level game AI agents with the win rate. The model includes the action space of Protoss, the observation space and the realization of DRL algorithms. Meanwhile, the model is built on PySC2 v2.0, which provides additional action functions. Due to the complexity and the unique characteristics of Protoss in StarCraft II, the model cannot be applied to other games or platforms. However, how the model trains a Protoss AI agent can show the limitation of DRL and push DRL algorithm a little forward.

Deep reinforcement learning is an area of research that has blossomed tremendously in recent years and has shown remarkable potential in computer games. Real-time strategy game has become an important field of artificial intelligence in game for several years. This paper is about to introduce a kind of algorithm that used to train agents to fight against computer bots. Not only because games are excellent tools to test deep reinforcement learning algorithms for their valuable insight into how well an algorithm can perform in isolated environments without the real-life consequences, but also real-time strategy games are a very complex genre that challenges artificial intelligence agents in both short-term or long-term planning. In this paper, we introduce some history of deep learning and reinforcement learning. Then we combine them with StarCraft. PPO is the algorithm which have some of the benefits of trust region policy optimization (TRPO), but it is much simpler to implement, more general for environment, and have better sample complexity. The StarCraft environment: Blood War Application Programming Interface (BWAPI) is open source to test. The results show that PPO can work well in BWAPI and train units to defeat the opponents. The algorithm presented in the thesis is corroborated by experiments.

Organisational culture is a long debated research field, one that is greatly influential in modern day workspace, possibly deeply affecting organisational performance. This thesis is a case study on Blizzard entertainment from 1997 to 2005, where Blizzard North and Blizzard South, two organisations, existed and worked on some of the company’s most influential games. The authors analysed and inspected the unique culture of each of the organisation, intending to understand the effect of organisational culture on video game development. The analysis is conducted utilising multiple organisational cultural theories and models. Both of the studios' organisational culture is explored in this study and the study aims to show the effect of these organisational cultures in the game development process. The importance of this research lies in studying the connection between organisational culture and the gaming development process. This research is for an audience which takes interest in starting their own company or working in one, people who want to understand how companies work and people who are interested to see different behaviours in different situations. The key findings of this study are about how organisational culture affects different aspects of game development such as design, approaches and relationships between peers.

This thesis investigates coordinating units through simultaneous coalition structuregeneration and task assignment in a complex Euclidean environment. The environmentused is StarCraft II, and the problem modeled and solved in the game is the distribution ofcombat units over the game’s map. The map was split into regions, and every region wasmodeled as a task to which the combat units were assigned.In a number of experiments, we compare the performance of our approach with thegame’s built-in bots. Against most of the non cheating options, our agent wins 20% of thegames played on a large map, against the Hard built-in bot. On a smaller and simpler mapit wins 22% of games played against the hardest non-cheating difficulty.One of the main limitations of the method used to solve the assignment was the utility function. Which should describe the quality of a coalition and the task assignment.However, as the utility function described the state’s utility better, the win rate increased.Therefore the result indicates that the simultaneous coalition structure generation and taskassignment work for unit distribution in a complex environment like StarCraft II if a sufficient utility function is provided.

This thesis outlines the structure and functionality of a selection of webseries, webshows, and eSports casting examples, in order to add to the discourse on online video. Webtelevision, or Web TV production, distribution, and financing systems will be detailed in the case studies made; and industry actors such as entrepreneurs, independents, corporations and conglomerates will be discussed and identified. Who are the producers, the advertisers, the distribution platforms, the sponsors, the rights holders, and how do they interact? In exploring the structure of some examples of Web TV, I wish to debunk the online-amateur association as an inaccurate or insufficient description which permeates much prior academic study on online video. Webshow content, business strategies, legal and copyright issues, as well as fan culture aspects will also be investigated; and in regards to eSports, the question of televised as opposed to streamcast tournaments will be examined.

The use of computer games has grown exponentially in the past few years and StarCraft II is one of the most popular e-sports today, played by millions of people worldwide. This study investigates the game’s effect on skills, as perceived by the players of StarCraft II. The study was conducted by using an empirical survey and interviews. The skills being investigated were both physical and mental in their nature. The conclusion was that the majority of players perceived that their skills regarding reaction speed, multitasking, APM, analysis and strategy were increased the most, while there was a smaller change in social skills and language skills. Additionally, people who played team games of StarCraft II did, however, feel that their teamwork, conflict management and leadership capabilities increased. / Under de senaste åren har användandet av datorspel vuxit explosionsartat. StarCraft II är ett utav dagens mest populära e-sporter och spelas av miljontals människor runt om i världen. Studien undersöker om spelare uppfattar kompetensförändringar av att spela StarCraft II. Studien utfördes med hjälp utav en enkät och intervjuer. Kompetenserna vi undersökte var av både fysiska och mentala i sin karaktär. Av resultaten kan man se att majoriteten av spelarna uppfattade en ökad kompetens inom reaktionshastighet, multitasking, APM, analytisk- och strategisk förmåga, medan man ser en mindre förändring inom social kompetens och språklig kompetens. Resultaten visar även att de som valde att spela lagspel uppfattade en ökad kompetens inom samarbete, konflikthantering och ledarskap.

Pour respecter les droits d’auteur, la version électronique de ce mémoire a été dépouillée de ses documents visuels et audio‐visuels. La version intégrale du mémoire a été déposée au Service de la gestion des documents et des archives de l'Université de Montréal. / Cette recherche propose une analyse du jeu de stratégie en temps réel StarCraft (Blizzard Entertainment, 1998). Il s’agit de questionner le concept de stratégie dans le jeu sans s’en tenir à ce qu’on peut voir et entendre. Ce mémoire débute sur une description du jeu en détails afin de faire ressortir comment la stratégie joue un rôle dans l’ensemble des compétences qui y sont mobilisées. Ensuite, le cercle heuristique du processus stratégique offre une modélisation du fonctionnement de la stratégie en tant que processus cognitif, basé sur les états du jeu inférés chez le joueur et sur ses plans stratégiques. Ce modèle et les concepts qui en découlent sont consolidés par des analyses de parties spécifiques de StarCraft. / This thesis offers an analysis of the Real-Time Strategy game StarCraft (Blizzard Entertainment, 1998). Its goal is to explore beyond the visible and audible part of the game to elucidate the concept of strategy into play. Following a description of the game and its constraints, it demonstrates how strategy plays a major role within the skills needed to play. Then, our “heuristic circle of the strategic process” describes how strategy works as a cognitive process, and how it interacts with both the game states inferred by the player and his or her strategic plans. Finally, this model and its underlying concepts are supported by close analyses of StarCraft game sequences.

Cette recherche propose une analyse du jeu de stratégie en temps réel StarCraft (Blizzard Entertainment, 1998). Il s’agit de questionner le concept de stratégie dans le jeu sans s’en tenir à ce qu’on peut voir et entendre. Ce mémoire débute sur une description du jeu en détails afin de faire ressortir comment la stratégie joue un rôle dans l’ensemble des compétences qui y sont mobilisées. Ensuite, le cercle heuristique du processus stratégique offre une modélisation du fonctionnement de la stratégie en tant que processus cognitif, basé sur les états du jeu inférés chez le joueur et sur ses plans stratégiques. Ce modèle et les concepts qui en découlent sont consolidés par des analyses de parties spécifiques de StarCraft. / This thesis offers an analysis of the Real-Time Strategy game StarCraft (Blizzard Entertainment, 1998). Its goal is to explore beyond the visible and audible part of the game to elucidate the concept of strategy into play. Following a description of the game and its constraints, it demonstrates how strategy plays a major role within the skills needed to play. Then, our “heuristic circle of the strategic process” describes how strategy works as a cognitive process, and how it interacts with both the game states inferred by the player and his or her strategic plans. Finally, this model and its underlying concepts are supported by close analyses of StarCraft game sequences. / Pour respecter les droits d’auteur, la version électronique de ce mémoire a été dépouillée de ses documents visuels et audio‐visuels. La version intégrale du mémoire a été déposée au Service de la gestion des documents et des archives de l'Université de Montréal.

The aim of this thesis is to create an automated system for playing a real-time strategy game Starcraft II. Learning from replays via supervised learning and reinforcement learning techniques are used for improving bot's behavior. The proposed system should be capable of playing the whole game utilizing PySC2 framework for machine learning. Performance of the bot is evaluated against the built-in scripted AI in the game.

This thesis is focused on designing and implementing system, that adds learning and planning capabilities to agents designed for playing real-time strategy games like StarCraft. It will explain problems of controlling game entities and bots by computer and introduce some often used solutions. Based on analysis, a new system has been designed and implemented. It uses multi-agent systems to control the game, utilizes machine learning methods and is capable of overcoming oponents and adapting to new challenges.