Explaining how to play real-time strategy games

Real-time strategy games share many aspects with real situations in domains such as battle planning, air traffic control, and emergency response team management which makes them appealing test-beds for Artificial Intelligence (AI) and machine learning. End-user annotations could help to provide supplemental information for learning algorithms, especially when training data is sparse. This paper presents a formative study to uncover how experienced users explain game play in real-time strategy games. We report the results of our analysis of explanations and discuss their characteristics that could support the design of systems for use by experienced real-time strategy game users in specifying or annotating strategy-oriented behavior

Deep learning for video game playing

In this article, we review recent Deep Learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games. We analyze the unique requirements that different game genres pose to a deep learning system and highlight important open challenges in the context of applying these machine learning methods to video games, such as general game playing, dealing with extremely large decision spaces and sparse rewards

Real-Time Evolutionary Learning of Cooperative Predator-Prey Strategies

Despite games often being used as a testbed for new computational intelligence techniques, the majority of artificial intelligence in commercial games is scripted. This means that the computer agents are non-adaptive and often inherently exploitable because of it. In this paper, we describe a learning system designed for team strategy development in a real time multi-agent domain. We test our system in a prey and predators domain, evolving adaptive team strategies for the predators in real time against a single prey opponent. Our learning system works by continually training and updating the predator strategies, one at a time for a designated length of time while the game us being played. We test the performance of the system for real-time learning of strategies in the prey and predators domain against a hand-coded prey opponent. We show that the resulting real-time team strategies are able to capture hand-coded prey of varying degrees of difficulty without any prior learning. The system is highly adaptive to change, capable of handling many different situations, and quickly learning to function in situations that it has never seen before

Q-learnings in RTs game's micro-management

Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2015, Director: Jesús Cerquides BuenoThe purpose of this Project is to implement the one-step Q-Learning algorithm and a similar version using linear function approximation in a combat scenario in the Real-Time Strategy game Starcraft: BroodwarTM. First, there is a brief description of Real-Time Strategy games, and particularly about Starcraft, and some of the work done in the field of Reinforcement Learning. After the introduction and previous work are covered, a description of the Reinforcement Learning problem in Real-Time Strategy games is shown. Then, the development of the Reinforcement Learning agents using Q-Learning and Approximate Q-Learning is explained. It is divided into three phases: the first phase consists of defining the task that the agents must solve as a Markov Decision Process and implementing the Reinforcement Learning agents. The second phase is the training period: the agents have to learn how to destroy the rival units and avoid being destroyed in a set of training maps. This will be done through exploration because the agents have no prior knowledge of the outcome of the available actions. The third and last phase is testing the agents’ knowledge acquired in the training period in a different set of maps, observing the results and finally comparing which agent has performed better. The expected behavior is that both Q-Learning agents will learn how to kite (attack and flee) in any combat scenario. Ultimately, this behavior could become the micro-management portion of a new Bot or could be added to an existing bot

Improved Reinforcement Learning in Asymmetric Real-time Strategy Games via Strategy Diversity

We investigate the use of artificial intelligence (AI)-based techniques in learning to play a 2-player, real-time strategy (RTS) game called Hunting-of-the-Plark. The game is challenging to play for both humans and AI-based techniques because players cannot observe each other's moves while playing the game and one player is at a disadvantage due to the asymmetric nature of the game rules. We analyze the performance of different deep reinforcement learning algorithms to train software agents that can play the game. Existing reinforcement learning techniques for RTS games enable players to converge towards an equilibrium outcome of the game but usually do not facilitate further exploration of techniques to exploit and defeat the opponent. To address this shortcoming, we investigate techniques including self-play and strategy diversity that can be used by players to improve their performance beyond the equilibrium outcome. We observe that when players use self-play, their number of wins begins to cycle around an equilibrium value as each player quickly learns to outwit and defeat its opponent and vice-versa. Finally, we show that strategy diversity could be used as an effective means to alleviate the performance of the disadvantaged player caused by the asymmetric nature of the game