Deep Reinforcement Learning for General Video Game AI

Torrado, Ruben Rodriguez; Bontrager, Philip; Togelius, Julian; Liu, Jialin; Peŕez-Liebana, Diego

doi:10.1109/cig.2018.8490422

Cited by 89 publications

(51 citation statements)

References 11 publications

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“…Game state information (same as in the planning case) is provided in a Json format and the game screen can be observed by the agent at every game tick. Since 2018, Torrado et al [15] interfaced the GVGAI framework to the OpenAI Gym environment.…”

Section: The Gvgai Frameworkmentioning

confidence: 99%

“…Game Game 1 Game 2 Game 3 Ranking Level 3 4 5 3 4 5 3 4 are not applied to the game 3, due to the different game screen dimensions of different levels. We would like to refer the readers to [15] for more about the GVGAI Gym.…”

Section: Contest Legmentioning

confidence: 99%

“…Using the new GVGAI Gym, Torrado et al [15] compared 3 implemented Deep Reinforcement Learning algorithms of the OpenAI Gym, Deep Q-Network (DQN), Prioritized Dueling DQN and Advance Actor-Critic (A2C), on 8 GVGAI games with various difficulties and game rules. All the three RL agents perform well on most of the games, however, DQNs and A2C perform badly when no game score is given during a game playing (only win or loss is given when a game terminates).…”

Section: B Other Learning Agentsmentioning

confidence: 99%

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General Video Game AI: A Multitrack Framework for Evaluating Agents, Games, and Content Generation Algorithms

et al. 2019

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General Video Game Playing (GVGP) aims at designing an agent that is capable of playing multiple video games with no human intervention. In 2014, The General Video Game AI (GVGAI) competition framework was created and released with the purpose of providing researchers a common open-source and easy to use platform for testing their AI methods with potentially infinity of games created using Video Game Description Language (VGDL). The framework has been expanded into several tracks during the last few years to meet the demand of different research directions. The agents are required either to play multiple unknown games with or without access to game simulations, or to design new game levels or rules. This survey paper presents the VGDL, the GVGAI framework, existing tracks, and reviews the wide use of GVGAI framework in research, education and competitions five years after its birth. A future plan of framework improvements is also described.

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Section: The Gvgai Frameworkmentioning

confidence: 99%

Section: Contest Legmentioning

confidence: 99%

Section: B Other Learning Agentsmentioning

confidence: 99%

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General Video Game AI: A Multitrack Framework for Evaluating Agents, Games, and Content Generation Algorithms

et al. 2019

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“…Whereas most successful approaches for GVGAI games employ MCTS, it shall be noted that there are also other competitive approaches as the rolling horizon evolutionary algorithm (RHEA) [42] that evolve partial action sequences as a whole through an evolutionary optimization process. Furthermore, DL variants start to get used here as well [83].…”

Section: Merging State and Pixel Informationmentioning

confidence: 99%

From Chess and Atari to StarCraft and Beyond: How Game AI is Driving the World of AI

Risi

Preuß

2020

Künstl Intell

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This paper reviews the field of Game AI, which not only deals with creating agents that can play a certain game, but also with areas as diverse as creating game content automatically, game analytics, or player modelling. While Game AI was for a long time not very well recognized by the larger scientific community, it has established itself as a research area for developing and testing the most advanced forms of AI algorithms and articles covering advances in mastering video games such as StarCraft 2 and Quake III appear in the most prestigious journals. Because of the growth of the field, a single review cannot cover it completely. Therefore, we put a focus on important recent developments, including that advances in Game AI are starting to be extended to areas outside of games, such as robotics or the synthesis of chemicals. In this article, we review the algorithms and methods that have paved the way for these breakthroughs, report on the other important areas of Game AI research, and also point out exciting directions for the future of Game AI.

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“…The GAN algorithm adopted in level generation has been applied on a pilot basis to a large number of dungeons and level generation [1][2][3]. Convolution neural network (CNN) or parameter-based reinforcement learning algorithms applied to nonplayable characters (NPC) are used to develop NPC that can learn user behavior patterns [4][5][6]. Learning algorithms for time-series data based on long-short term memory (LSTM) used in traditional user log analysis are applied to technologies such as user churn prediction [7,8] or bot detection [9].…”

Section: Introductionmentioning

confidence: 99%

Instance Segmentation Method of User Interface Component of Games

Kang

Choi

2020

Applied Sciences

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On the game screen, the UI interface provides key information for game play. A vision deep learning network exploits pure pixel information in the screen. Apart from this, if we separately extract the information provided by the UI interface and use it as an additional input value, we can enhance the learning efficiency of deep learning networks. To this end, by effectively segmenting UI interface components such as buttons, image icons, and gauge bars on the game screen, we should be able to separately analyze only the relevant images. In this paper, we propose a methodology that segments UI components in a game by using synthetic game images created on a game engine. We developed a tool that approximately detected the UI areas of an image in games on the game screen and generated a large amount of synthetic labeling data through this. By training this data on a Pix2Pix, we applied UI segmentation. The network trained in this way can segment the UI areas of the target game regardless of the position of the corresponding UI components. Our methodology can help analyze the game screen without applying data augmentation to the game screen. It can also help vision researchers who should extract semantic information from game image data.

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Deep Reinforcement Learning for General Video Game AI

Cited by 89 publications

References 11 publications

General Video Game AI: A Multitrack Framework for Evaluating Agents, Games, and Content Generation Algorithms

General Video Game AI: A Multitrack Framework for Evaluating Agents, Games, and Content Generation Algorithms

From Chess and Atari to StarCraft and Beyond: How Game AI is Driving the World of AI

Instance Segmentation Method of User Interface Component of Games

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