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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 76–100 of 311 papers

TitleStatusHype
Real World Games Look Like Spinning TopsCode1
FACMAC: Factored Multi-Agent Centralised Policy GradientsCode1
DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial NetsCode1
Meta Reinforcement Learning with Autonomous Inference of Subtask DependenciesCode1
LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement LearningCode1
The StarCraft Multi-Agent ChallengeCode1
Towards Accurate Generative Models of Video: A New Metric & ChallengesCode1
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement LearningCode1
Counterfactual Multi-Agent Policy GradientsCode1
Stabilising Experience Replay for Deep Multi-Agent Reinforcement LearningCode1
Transformer World Model for Sample Efficient Multi-Agent Reinforcement LearningCode0
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
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
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
Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement LearningCode0
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