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Starcraft

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

( Image credit: Macro Action Selection with Deep Reinforcement Learning in StarCraft )

Papers

Showing 125 of 311 papers

TitleStatusHype
Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement LearningCode2
Dungeons and Data: A Large-Scale NetHack DatasetCode2
Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization ApproachCode2
On Efficient Reinforcement Learning for Full-length Game of StarCraft IICode2
A New Approach to Solving SMAC Task: Generating Decision Tree Code from Large Language ModelsCode2
AlphaStar Unplugged: Large-Scale Offline Reinforcement LearningCode2
Efficient Episodic Memory Utilization of Cooperative Multi-Agent Reinforcement LearningCode2
JaxMARL: Multi-Agent RL Environments and Algorithms in JAXCode2
Maximum Entropy Heterogeneous-Agent Reinforcement LearningCode2
SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement LearningCode2
LLM-PySC2: Starcraft II learning environment for Large Language ModelsCode2
ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-DependencyCode2
Hierarchical Expert Prompt for Large-Language-Model: An Approach Defeat Elite AI in TextStarCraft II for the First TimeCode2
Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team CompositionCode1
Randomized Entity-wise Factorization for Multi-Agent Reinforcement LearningCode1
DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial NetsCode1
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
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-LearningCode1
Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement LearningCode1
Applying supervised and reinforcement learning methods to create neural-network-based agents for playing StarCraft IICode1
Celebrating Diversity in Shared Multi-Agent Reinforcement LearningCode1
C-COMA: A CONTINUAL REINFORCEMENT LEARNING MODEL FOR DYNAMIC MULTIAGENT ENVIRONMENTSCode1
Coordinated Proximal Policy OptimizationCode1
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