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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 101–125 of 311 papers

TitleStatusHype
Data-Driven Distributed Common Operational Picture from Heterogeneous Platforms using Multi-Agent Reinforcement Learning—0
Offline-to-Online Multi-Agent Reinforcement Learning with Offline Value Function Memory and Sequential Exploration—0
Efficiently Scanning and Resampling Spatio-Temporal Tasks with Irregular Observations—0
Carefully Structured Compression: Efficiently Managing StarCraft II DataCode0
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
Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World ModelsCode0
Tree Search for Simultaneous Move Games via Equilibrium Approximation—0
Variational Offline Multi-agent Skill Discovery—0
POWQMIX: Weighted Value Factorization with Potentially Optimal Joint Actions Recognition for Cooperative Multi-Agent Reinforcement Learning—0
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
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
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
BET: Explaining Deep Reinforcement Learning through The Error-Prone Decisions—0
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