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Starcraft

Starcraft I is a RTS game; the task is to train an agent to play the game.

( Image credit: Macro Action Selection with Deep Reinforcement Learning in StarCraft )

Papers

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Showing 71–80 of 311 papers

TitleStatusHype
Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value RegularizationCode1
Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC TasksCode1
DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial NetsCode1
Energy-based Surprise Minimization for Multi-Agent Value FactorizationCode1
Real World Games Look Like Spinning TopsCode1
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-LearningCode1
C-COMA: A CONTINUAL REINFORCEMENT LEARNING MODEL FOR DYNAMIC MULTIAGENT ENVIRONMENTSCode1
PushWorld: A benchmark for manipulation planning with tools and movable obstaclesCode1
SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-LearningCode1
Transformer-based Value Function Decomposition for Cooperative Multi-agent Reinforcement Learning in StarCraftCode1
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