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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

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Showing 1–50 of 311 papers

TitleStatusHype
Hierarchical Expert Prompt for Large-Language-Model: An Approach Defeat Elite AI in TextStarCraft II for the First TimeCode2
LLM-PySC2: Starcraft II learning environment for Large Language ModelsCode2
A New Approach to Solving SMAC Task: Generating Decision Tree Code from Large Language ModelsCode2
Efficient Episodic Memory Utilization of Cooperative Multi-Agent Reinforcement LearningCode2
Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization ApproachCode2
JaxMARL: Multi-Agent RL Environments and Algorithms in JAXCode2
AlphaStar Unplugged: Large-Scale Offline Reinforcement LearningCode2
Maximum Entropy Heterogeneous-Agent Reinforcement LearningCode2
SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement LearningCode2
ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-DependencyCode2
Dungeons and Data: A Large-Scale NetHack DatasetCode2
On Efficient Reinforcement Learning for Full-length Game of StarCraft IICode2
Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement LearningCode2
A Benchmark for Generalizing Across Diverse Team Strategies in Competitive PokémonCode1
AVA: Attentive VLM Agent for Mastering StarCraft IICode1
Trajectory-Class-Aware Multi-Agent Reinforcement LearningCode1
SMAC-Hard: Enabling Mixed Opponent Strategy Script and Self-play on SMACCode1
Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement LearningCode1
Assigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy OptimizationCode1
Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement LearningCode1
Group-Aware Coordination Graph for Multi-Agent Reinforcement LearningCode1
N-Agent Ad Hoc TeamworkCode1
SwarmBrain: Embodied agent for real-time strategy game StarCraft II via large language modelsCode1
FoX: Formation-aware exploration in multi-agent reinforcement learningCode1
Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value RegularizationCode1
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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