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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 76–100 of 175 papers

TitleStatusHype
Depthwise Convolution for Multi-Agent Communication with Enhanced Mean-Field Approximation—0
MCMARL: Parameterizing Value Function via Mixture of Categorical Distributions for Multi-Agent Reinforcement LearningCode0
FCMNet: Full Communication Memory Net for Team-Level Cooperation in Multi-Agent SystemsCode0
Value Functions Factorization with Latent State Information Sharing in Decentralized Multi-Agent Policy GradientsCode0
Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning—0
CGIBNet: Bandwidth-constrained Communication with Graph Information Bottleneck in Multi-Agent Reinforcement Learning—0
Cooperative Multi-Agent Reinforcement Learning with Hypergraph ConvolutionCode0
Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC TasksCode1
Regularized Softmax Deep Multi-Agent Q-LearningCode1
RMIX: Learning Risk-Sensitive Policies forCooperative Reinforcement Learning Agents—0
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven ExplorationCode1
Coordinated Proximal Policy OptimizationCode1
On games and simulators as a platform for development of artificial intelligence for command and control—0
Containerized Distributed Value-Based Multi-Agent Reinforcement Learning—0
HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanism—0
Leveraging Transformers for StarCraft Macromanagement Prediction—0
TiKick: Towards Playing Multi-agent Football Full Games from Single-agent DemonstrationsCode1
Role Diversity Matters: A Study of Cooperative Training Strategies for Multi-Agent RL—0
MARNET: Backdoor Attacks against Value-Decomposition Multi-Agent Reinforcement Learning—0
Applying supervised and reinforcement learning methods to create neural-network-based agents for playing StarCraft IICode1
An Approach to Partial Observability in Games: Learning to Both Act and Observe—0
Rethinking of AlphaStarCode1
Perceiver IO: A General Architecture for Structured Inputs & OutputsCode1
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement LearningCode1
Context-Aware Sparse Deep Coordination GraphsCode1
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