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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 101–150 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
S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning?—0
S2RMs: Spatially Structured Recurrent Modules—0
SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II—0
Taming Multi-Agent Reinforcement Learning with Estimator Variance Reduction—0
Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning—0
SIDE: State Inference for Partially Observable Cooperative Multi-Agent Reinforcement Learning—0
SMAUG: A Sliding Multidimensional Task Window-Based MARL Framework for Adaptive Real-Time Subtask Recognition—0
Regularized Softmax Deep Multi-Agent Q-Learning—0
Spatially Structured Recurrent Modules—0
Spatially Structured Recurrent Modules—0
StarCraft II Build Order Optimization using Deep Reinforcement Learning and Monte-Carlo Tree Search—0
StarCraftImage: A Dataset For Prototyping Spatial Reasoning Methods For Multi-Agent Environments—0
Superhuman Game AI Disclosure: Expertise and Context Moderate Effects on Trust and Fairness—0
SVDE: Scalable Value-Decomposition Exploration for Cooperative Multi-Agent Reinforcement Learning—0
Towards a Deep Reinforcement Learning Approach for Tower Line Wars—0
Towards Understanding Cooperative Multi-Agent Q-Learning with Value Factorization—0
Towards Understanding Linear Value Decomposition in Cooperative Multi-Agent Q-Learning—0
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning—0
Unsupervised Hebbian Learning on Point Sets in StarCraft II—0
Value Functions Factorization with Latent State Information Sharing in Decentralized Multi-Agent Policy GradientsCode0
CODEX: A Cluster-Based Method for Explainable Reinforcement LearningCode0
Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed AgentsCode0
Carefully Structured Compression: Efficiently Managing StarCraft II DataCode0
StarCraft II: A New Challenge for Reinforcement LearningCode0
PAC: Assisted Value Factorisation with Counterfactual Predictions in Multi-Agent Reinforcement LearningCode0
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