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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

Showing 125 of 175 papers

TitleStatusHype
AlphaStar Unplugged: Large-Scale Offline Reinforcement LearningCode2
Efficient Episodic Memory Utilization of Cooperative Multi-Agent Reinforcement LearningCode2
Hierarchical Expert Prompt for Large-Language-Model: An Approach Defeat Elite AI in TextStarCraft II for the First TimeCode2
JaxMARL: Multi-Agent RL Environments and Algorithms in JAXCode2
Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization ApproachCode2
On Efficient Reinforcement Learning for Full-length Game of StarCraft IICode2
LLM-PySC2: Starcraft II learning environment for Large Language ModelsCode2
Gym-μRTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement LearningCode1
Graph Convolutional Value Decomposition in Multi-Agent Reinforcement LearningCode1
Group-Aware Coordination Graph for Multi-Agent Reinforcement LearningCode1
Context-Aware Sparse Deep Coordination GraphsCode1
Assigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy OptimizationCode1
Energy-based Surprise Minimization for Multi-Agent Value FactorizationCode1
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority InfluenceCode1
An Introduction of mini-AlphaStarCode1
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven ExplorationCode1
Deep Implicit Coordination Graphs for Multi-agent Reinforcement LearningCode1
Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement LearningCode1
FACMAC: Factored Multi-Agent Centralised Policy GradientsCode1
C-COMA: A CONTINUAL REINFORCEMENT LEARNING MODEL FOR DYNAMIC MULTIAGENT ENVIRONMENTSCode1
Celebrating Diversity in Shared Multi-Agent Reinforcement LearningCode1
Applying supervised and reinforcement learning methods to create neural-network-based agents for playing StarCraft IICode1
Coordinated Proximal Policy OptimizationCode1
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit AssignmentCode1
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement LearningCode1
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