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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 1–25 of 311 papers

TitleStatusHype
Transformer World Model for Sample Efficient Multi-Agent Reinforcement LearningCode0
A Benchmark for Generalizing Across Diverse Team Strategies in Competitive PokémonCode1
NeuroPAL: Punctuated Anytime Learning with Neuroevolution for Macromanagement in Starcraft: Brood War—0
Language-Guided Multi-Agent Learning in Simulations: A Unified Framework and Evaluation—0
Dynamic Sight Range Selection in Multi-Agent Reinforcement Learning—0
AVA: Attentive VLM Agent for Mastering StarCraft IICode1
Trajectory-Class-Aware Multi-Agent Reinforcement LearningCode1
SrSv: Integrating Sequential Rollouts with Sequential Value Estimation for Multi-agent Reinforcement Learning—0
Nucleolus Credit Assignment for Effective Coalitions in Multi-agent Reinforcement Learning—0
PMAT: Optimizing Action Generation Order in Multi-Agent Reinforcement LearningCode0
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
Data-Driven Distributed Common Operational Picture from Heterogeneous Platforms using Multi-Agent Reinforcement Learning—0
LLM-PySC2: Starcraft II learning environment for Large Language ModelsCode2
Offline-to-Online Multi-Agent Reinforcement Learning with Offline Value Function Memory and Sequential Exploration—0
A New Approach to Solving SMAC Task: Generating Decision Tree Code from Large Language ModelsCode2
Efficiently Scanning and Resampling Spatio-Temporal Tasks with Irregular Observations—0
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