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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 1–50 of 311 papers

TitleStatusHype
Transformer World Model for Sample Efficient Multi-Agent Reinforcement LearningCode0
NeuroPAL: Punctuated Anytime Learning with Neuroevolution for Macromanagement in Starcraft: Brood War—0
A Benchmark for Generalizing Across Diverse Team Strategies in Competitive PokémonCode1
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
Carefully Structured Compression: Efficiently Managing StarCraft II DataCode0
Efficiently Scanning and Resampling Spatio-Temporal Tasks with Irregular Observations—0
Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement LearningCode1
Grounded Answers for Multi-agent Decision-making Problem through Generative World Model—0
ComaDICE: Offline Cooperative Multi-Agent Reinforcement Learning with Stationary Distribution Shift Regularization—0
On Stateful Value Factorization in Multi-Agent Reinforcement Learning—0
Hybrid Training for Enhanced Multi-task Generalization in Multi-agent Reinforcement Learning—0
Assigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy OptimizationCode1
Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World ModelsCode0
Tree Search for Simultaneous Move Games via Equilibrium Approximation—0
Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement LearningCode1
Variational Offline Multi-agent Skill Discovery—0
POWQMIX: Weighted Value Factorization with Potentially Optimal Joint Actions Recognition for Cooperative Multi-Agent Reinforcement Learning—0
Group-Aware Coordination Graph for Multi-Agent Reinforcement LearningCode1
N-Agent Ad Hoc TeamworkCode1
Heterogeneous Multi-Agent Reinforcement Learning for Zero-Shot Scalable Collaboration—0
MARL-LNS: Cooperative Multi-agent Reinforcement Learning via Large Neighborhoods Search—0
Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement LearningCode0
Collaborative AI Teaming in Unknown Environments via Active Goal Deduction—0
Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning—0
PPS-QMIX: Periodically Parameter Sharing for Accelerating Convergence of Multi-Agent Reinforcement LearningCode0
SMAUG: A Sliding Multidimensional Task Window-Based MARL Framework for Adaptive Real-Time Subtask Recognition—0
Efficient Episodic Memory Utilization of Cooperative Multi-Agent Reinforcement LearningCode2
Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning—0
Aligning Individual and Collective Objectives in Multi-Agent Cooperation—0
Enabling Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation—0
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