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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 2650 of 311 papers

TitleStatusHype
Gym-μRTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement LearningCode1
Celebrating Diversity in Shared Multi-Agent Reinforcement LearningCode1
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority InfluenceCode1
Energy-based Surprise Minimization for Multi-Agent Value FactorizationCode1
DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial NetsCode1
Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?Code1
FoX: Formation-aware exploration in multi-agent reinforcement learningCode1
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement LearningCode1
Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement LearningCode1
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-LearningCode1
Graph Convolutional Value Decomposition in Multi-Agent Reinforcement LearningCode1
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit AssignmentCode1
C-COMA: A CONTINUAL REINFORCEMENT LEARNING MODEL FOR DYNAMIC MULTIAGENT ENVIRONMENTSCode1
A Benchmark for Generalizing Across Diverse Team Strategies in Competitive PokémonCode1
Coordinated Proximal Policy OptimizationCode1
An Introduction of mini-AlphaStarCode1
Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team CompositionCode1
Applying supervised and reinforcement learning methods to create neural-network-based agents for playing StarCraft IICode1
Rethinking the Implementation Matters in Cooperative Multi-Agent Reinforcement LearningCode1
Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement LearningCode1
Deep Implicit Coordination Graphs for Multi-agent Reinforcement LearningCode1
Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement LearningCode1
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement LearningCode1
Context-Aware Sparse Deep Coordination GraphsCode1
Counterfactual Multi-Agent Policy GradientsCode1
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