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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 150 of 311 papers

TitleStatusHype
Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement LearningCode2
Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization ApproachCode2
SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement LearningCode2
Efficient Episodic Memory Utilization of Cooperative Multi-Agent Reinforcement LearningCode2
ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-DependencyCode2
On Efficient Reinforcement Learning for Full-length Game of StarCraft IICode2
Hierarchical Expert Prompt for Large-Language-Model: An Approach Defeat Elite AI in TextStarCraft II for the First TimeCode2
Dungeons and Data: A Large-Scale NetHack DatasetCode2
Maximum Entropy Heterogeneous-Agent Reinforcement LearningCode2
LLM-PySC2: Starcraft II learning environment for Large Language ModelsCode2
AlphaStar Unplugged: Large-Scale Offline Reinforcement LearningCode2
JaxMARL: Multi-Agent RL Environments and Algorithms in JAXCode2
A New Approach to Solving SMAC Task: Generating Decision Tree Code from Large Language ModelsCode2
Meta Reinforcement Learning with Autonomous Inference of Subtask DependenciesCode1
Randomized Entity-wise Factorization for Multi-Agent Reinforcement LearningCode1
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven ExplorationCode1
Assigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy OptimizationCode1
LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement LearningCode1
MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay BufferCode1
Multi-Agent Collaboration via Reward Attribution DecompositionCode1
FACMAC: Factored Multi-Agent Centralised Policy GradientsCode1
Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement LearningCode1
Learning to Play No-Press Diplomacy with Best Response Policy IterationCode1
Effective and Stable Role-Based Multi-Agent Collaboration by Structural Information PrinciplesCode1
Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?Code1
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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