StarCraft Multi-Agent Challenge
Emerging18papers using it
2024first seen
The StarCraft Multi-Agent Challenge is a benchmark that contains diverse multi-agent environments used to evaluate the performance of reinforcement learning models across various tasks.
Papers using StarCraft Multi-Agent Challenge (18)
- SrSv: Integrating Sequential Rollouts with Sequential Value Estimation
for Multi-agent Reinforcement LearningAOAD-MAT: Transformer-based multi-agent deep reinforcement learning model considering agents' order of action decisionsMARL-GPT: Foundation Model for Multi-Agent Reinforcement LearningMulti-Agent Deep Reinforcement Learning Under Constrained CommunicationsStarCraft+: Benchmarking Multi-agent Algorithms in Adversary ParadigmHCPO: Hierarchical Conductor-Based Policy Optimization in Multi-Agent Reinforcement LearningTransformer World Model for Sample Efficient Multi-Agent Reinforcement LearningDynamic Sight Range Selection in Multi-Agent Reinforcement LearningAdaptive Episode Length Adjustment for Multi-agent Reinforcement LearningLow-Rank Agent-Specific Adaptation (LoRASA) for Multi-Agent Policy
LearningReducing Variance Caused by Communication in Decentralized Multi-agent
Deep Reinforcement LearningPMAT: Optimizing Action Generation Order in Multi-Agent Reinforcement
LearningPOWQMIX: Weighted Value Factorization With Potentially Optimal Joint Actions Recognition For Cooperative Multi-agent Reinforcement LearningMARL-LNS: Cooperative Multi-agent Reinforcement Learning via Large
Neighborhoods SearchGrounded Answers for Multi-agent Decision-making Problem through
Generative World ModelKaleidoscope: Learnable Masks for Heterogeneous Multi-agent
Reinforcement LearningB2MAPO: A Batch-by-Batch Multi-Agent Policy Optimization to Balance
Performance and EfficiencyHybrid Training for Enhanced Multi-task Generalization in Multi-agent Reinforcement Learning