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We present TorchCraft, a library that enables deep learning research on Real-Time Strategy (RTS) games such as StarCraft: Brood War, by making it easier to control these games from a machine learning framework, here Torch.
Torcs, the open racing car simulator
2000
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Rts games and real-time ai research
2004
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Learning to win: Case-based plan selection in a real-time strategy game
2005
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Case-based planning and execution for real-time strategy games
2007
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A turing test for computer game bots
2009
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A data mining approach to strategy prediction
2009
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The 2009 mario ai competition
2010
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Torch7: A matlab-like environment for machine learning
2011
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The arcade learning environment: An evaluation platform for general agents
2012
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Fast heuristic search for rts game combat scenarios
2012
Cited alongside, same era.
Bayesian programming and learning for multi-player video games: application to RTS AI
2012
Cited alongside, same era.
A dataset for starcraft ai & an example of armies clustering
2012
Cited alongside, same era.
Applying reinforcement learning to small scale combat in the real-time strategy game starcraft: broodwar
2012
Cited alongside, same era.
A survey of real-time strategy game ai research and competition in starcraft
2013
Cited alongside, same era.
A review of real-time strategy game ai
2014
Cited alongside, same era.
Deep learning
2015
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Human-level control through deep reinforcement learning
2015
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Starcraft brood war data mining
2015
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2016
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Starcraft ai competition
2016
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Heuristic Search Techniques for Real-Time Strategy Games
2016
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The malmo platform for artificial intelligence experimentation
2016
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Game-tree search over high-level game states in rts games
2014
Cited alongside, same era.
Reactive planning for micromanagement in rts games
2014
Cited alongside, same era.
https://bwapi.github.io/ , 2009–2015
BWAPI: Brood war api, an api for interacting with starcraft: Broodwar (1.16.1) · 2015
Cited alongside, same era.
Vizdoom: A doom-based ai research platform for visual reinforcement learning
2016
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