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Starcraft II

Starcraft II is a RTS game; the task is to train an agent to play the game.

( Image credit: The StarCraft Multi-Agent Challenge )

Papers

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Showing 101–125 of 175 papers

TitleStatusHype
Leveraging World Model Disentanglement in Value-Based Multi-Agent Reinforcement Learning—0
Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning—0
MARNET: Backdoor Attacks against Value-Decomposition Multi-Agent Reinforcement Learning—0
Minimax Exploiter: A Data Efficient Approach for Competitive Self-Play—0
A Limited-Capacity Minimax Theorem for Non-Convex Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets—0
Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization—0
MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees—0
Modular Architecture for StarCraft II with Deep Reinforcement Learning—0
CGIBNet: Bandwidth-constrained Communication with Graph Information Bottleneck in Multi-Agent Reinforcement Learning—0
Multi-Agent Deep Reinforcement Learning using Attentive Graph Neural Architectures for Real-Time Strategy Games—0
BGC: Multi-Agent Group Belief with Graph Clustering—0
Never Explore Repeatedly in Multi-Agent Reinforcement Learning—0
NQMIX: Non-monotonic Value Function Factorization for Deep Multi-Agent Reinforcement Learning—0
Off-Beat Multi-Agent Reinforcement Learning—0
Grounding Natural Language Commands to StarCraft II Game States for Narration-Guided Reinforcement Learning—0
On games and simulators as a platform for development of artificial intelligence for command and control—0
On Reinforcement Learning for Full-length Game of StarCraft—0
On Stateful Value Factorization in Multi-Agent Reinforcement Learning—0
POWQMIX: Weighted Value Factorization with Potentially Optimal Joint Actions Recognition for Cooperative Multi-Agent Reinforcement Learning—0
Reflection of Episodes: Learning to Play Game from Expert and Self Experiences—0
Reinforcement Learning for the Beginning of Starcraft II Game—0
RMIX: Learning Risk-Sensitive Policies forCooperative Reinforcement Learning Agents—0
RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents—0
RMIX: Risk-Sensitive Multi-Agent Reinforcement Learning—0
Role Diversity Matters: A Study of Cooperative Training Strategies for Multi-Agent RL—0
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