A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft

Ontañón, Santiago; Synnaeve, Gabriel; Uriarte, Alberto; Richoux, Florian; Churchill, David G.; Preuß, Mike

doi:10.1109/tciaig.2013.2286295

Cited by 322 publications

(277 citation statements)

References 33 publications

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“…This is the case in Minecraft (Project Malmo; Johnson et al, 2016), Starcraft (Ontañón et al, 2013), and robotic RL platforms (Sutton et al, 2011).…”

Section: Asynchronous Environmentmentioning

confidence: 99%

Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents

Machado¹,

Bellemare²,

Talvitie³

et al. 2018

jair

213

201

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The Arcade Learning Environment (ALE) is an evaluation platform that poses the challenge of building AI agents with general competency across dozens of Atari 2600 games. It supports a variety of different problem settings and it has been receiving increasing attention from the scientific community, leading to some high-profile success stories such as the much publicized Deep Q-Networks (DQN). In this article we take a big picture look at how the ALE is being used by the research community. We show how diverse the evaluation methodologies in the ALE have become with time, and highlight some key concerns when evaluating agents in the ALE. We use this discussion to present some methodological best practices and provide new benchmark results using these best practices. To further the progress in the field, we introduce a new version of the ALE that supports multiple game modes and provides a form of stochasticity we call sticky actions. We conclude this big picture look by revisiting challenges posed when the ALE was introduced, summarizing the state-of-the-art in various problems and highlighting problems that remain open.

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“…This is the case in Minecraft (Project Malmo; Johnson et al, 2016), Starcraft (Ontañón et al, 2013), and robotic RL platforms (Sutton et al, 2011).…”

Section: Asynchronous Environmentmentioning

confidence: 99%

Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents

Machado¹,

Bellemare²,

Talvitie³

et al. 2018

jair

213

201

View full text Add to dashboard Cite

show abstract

“…For most games of the genre, one generally has to generate resources [2] and to use them for creating buildings and buildings that spawn additional units. The units usually consist of different builder or farmers and combat-oriented different types of soldiers [5]. Proper time management, intuitive use of forces and capabilities are compulsory, as one continuously tries upgrading and increasing bases and forces while putting pressure on the enemies, who are trying to do the same things.…”

Section: Real-time Strategy (Rts) Gamesmentioning

confidence: 99%

“…Its very first version was released in 1998 by Blizzard entertainment [2]. Starcraft is a science fiction based universe game introduces different combat teams like "Protoss", "Terran", and "Zerg" [5]. A typical match GUI of an RTS game Starcraft is shown in Fig.…”

Section: Starcraftmentioning

confidence: 99%

“…Some prevailing efforts on AI for real-time strategy games are achieved in [5], particularly the efforts about the Starcraft which has appeared in the recent past as an incorporated test bed for such type of research area, and specifically AI problems faced by RTS games and the solutions that have been proposed to address them are overviewed.…”

Section: State-of-the-art and Challengesmentioning

confidence: 99%

“…an open problem [2]. 2) A holistic approach, techniques that scale up to large RTS games as StarCraft [5].…”

Section: An Rts Combat Game Decision Treementioning

confidence: 99%

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