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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 51–75 of 311 papers

TitleStatusHype
MAIDCRL: Semi-centralized Multi-Agent Influence Dense-CNN Reinforcement Learning—0
COA-GPT: Generative Pre-trained Transformers for Accelerated Course of Action Development in Military Operations—0
SwarmBrain: Embodied agent for real-time strategy game StarCraft II via large language modelsCode1
BET: Explaining Deep Reinforcement Learning through The Error-Prone Decisions—0
Innate-Values-driven Reinforcement Learning based Cooperative Multi-Agent Cognitive Modeling—0
StarCraftImage: A Dataset For Prototyping Spatial Reasoning Methods For Multi-Agent Environments—0
Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization ApproachCode2
DCIR: Dynamic Consistency Intrinsic Reward for Multi-Agent Reinforcement Learning—0
CODEX: A Cluster-Based Method for Explainable Reinforcement LearningCode0
Minimax Exploiter: A Data Efficient Approach for Competitive Self-Play—0
JaxMARL: Multi-Agent RL Environments and Algorithms in JAXCode2
QFree: A Universal Value Function Factorization for Multi-Agent Reinforcement Learning—0
Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization—0
Privacy-Engineered Value Decomposition Networks for Cooperative Multi-Agent Reinforcement Learning—0
Fidelity-Induced Interpretable Policy Extraction for Reinforcement Learning—0
Leveraging World Model Disentanglement in Value-Based Multi-Agent Reinforcement Learning—0
FoX: Formation-aware exploration in multi-agent reinforcement learningCode1
Never Explore Repeatedly in Multi-Agent Reinforcement Learning—0
AlphaStar Unplugged: Large-Scale Offline Reinforcement LearningCode2
Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value RegularizationCode1
Anticipatory Thinking Challenges in Open Worlds: Risk Management—0
Transferable Curricula through Difficulty Conditioned Generators—0
Maximum Entropy Heterogeneous-Agent Reinforcement LearningCode2
Semantic HELM: A Human-Readable Memory for Reinforcement LearningCode1
Offline Multi-Agent Reinforcement Learning with Coupled Value Factorization—0
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