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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 11–20 of 311 papers

TitleStatusHype
Reflection of Episodes: Learning to Play Game from Expert and Self Experiences—0
Hierarchical Expert Prompt for Large-Language-Model: An Approach Defeat Elite AI in TextStarCraft II for the First TimeCode2
Cooperative Multi-Agent Planning with Adaptive Skill Synthesis—0
Low-Rank Agent-Specific Adaptation (LoRASA) for Multi-Agent Policy Learning—0
Innovative activities of Activision Blizzard: A patent network analysis—0
Superhuman Game AI Disclosure: Expertise and Context Moderate Effects on Trust and Fairness—0
Tackling Uncertainties in Multi-Agent Reinforcement Learning through Integration of Agent Termination DynamicsCode0
Human-like Bots for Tactical Shooters Using Compute-Efficient Sensors—0
SMAC-Hard: Enabling Mixed Opponent Strategy Script and Self-play on SMACCode1
Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement LearningCode0
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