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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
Semantic HELM: A Human-Readable Memory for Reinforcement LearningCode1
Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?Code1
SMAClite: A Lightweight Environment for Multi-Agent Reinforcement LearningCode1
Effective and Stable Role-Based Multi-Agent Collaboration by Structural Information PrinciplesCode1
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority InfluenceCode1
PushWorld: A benchmark for manipulation planning with tools and movable obstaclesCode1
TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning ProblemsCode1
Transformer-based Value Function Decomposition for Cooperative Multi-agent Reinforcement Learning in StarCraftCode1
SC2EGSet: StarCraft II Esport Replay and Game-state DatasetCode1
MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay BufferCode1
QGNN: Value Function Factorisation with Graph Neural NetworksCode1
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement LearningCode1
Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement LearningCode1
Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC TasksCode1
Regularized Softmax Deep Multi-Agent Q-LearningCode1
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven ExplorationCode1
Coordinated Proximal Policy OptimizationCode1
TiKick: Towards Playing Multi-agent Football Full Games from Single-agent DemonstrationsCode1
No-Press Diplomacy from ScratchCode1
Applying supervised and reinforcement learning methods to create neural-network-based agents for playing StarCraft IICode1
Settling the Variance of Multi-Agent Policy GradientsCode1
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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