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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
Learning to Play No-Press Diplomacy with Best Response Policy IterationCode1
Graph Convolutional Value Decomposition in Multi-Agent Reinforcement LearningCode1
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority InfluenceCode1
MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay BufferCode1
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven ExplorationCode1
Energy-based Surprise Minimization for Multi-Agent Value FactorizationCode1
FoX: Formation-aware exploration in multi-agent reinforcement learningCode1
Rethinking the Implementation Matters in Cooperative Multi-Agent Reinforcement LearningCode1
C-COMA: A CONTINUAL REINFORCEMENT LEARNING MODEL FOR DYNAMIC MULTIAGENT ENVIRONMENTSCode1
DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial NetsCode1
FACMAC: Factored Multi-Agent Centralised Policy GradientsCode1
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-LearningCode1
Effective and Stable Role-Based Multi-Agent Collaboration by Structural Information PrinciplesCode1
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement LearningCode1
Celebrating Diversity in Shared Multi-Agent Reinforcement LearningCode1
An Introduction of mini-AlphaStarCode1
Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team CompositionCode1
A Benchmark for Generalizing Across Diverse Team Strategies in Competitive PokémonCode1
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit AssignmentCode1
Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement LearningCode1
Group-Aware Coordination Graph for Multi-Agent Reinforcement LearningCode1
Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement LearningCode1
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement LearningCode1
Context-Aware Sparse Deep Coordination GraphsCode1
Deep Implicit Coordination Graphs for Multi-agent Reinforcement LearningCode1
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