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Starcraft II
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Starcraft II is a RTS game; the task is to train an agent to play the game.
<span style="color:grey; opacity: 0.6">( Image credit: The StarCraft Multi-Agent Challenge )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
AVA: Attentive VLM Agent for Mastering StarCraft II
Trajectory-Class-Aware Multi-Agent Reinforcement Learning
PMAT: Optimizing Action Generation Order in Multi-Agent Reinforcement Learning
Reflection of Episodes: Learning to Play Game from Expert and Self Experiences
Hierarchical Expert Prompt for Large-Language-Model: An Approach Defeat Elite AI in TextStarCraft II for the First Time
Superhuman Game AI Disclosure: Expertise and Context Moderate Effects on Trust and Fairness
Tackling Uncertainties in Multi-Agent Reinforcement Learning through Integration of Agent Termination Dynamics
Human-like Bots for Tactical Shooters Using Compute-Efficient Sensors
Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning
LLM-PySC2: Starcraft II learning environment for Large Language Models
Carefully Structured Compression: Efficiently Managing StarCraft II Data
Efficiently Scanning and Resampling Spatio-Temporal Tasks with Irregular Observations
ComaDICE: Offline Cooperative Multi-Agent Reinforcement Learning with Stationary Distribution Shift Regularization
On Stateful Value Factorization in Multi-Agent Reinforcement Learning
Assigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy Optimization
POWQMIX: Weighted Value Factorization with Potentially Optimal Joint Actions Recognition for Cooperative Multi-Agent Reinforcement Learning
Group-Aware Coordination Graph for Multi-Agent Reinforcement Learning
N-Agent Ad Hoc Teamwork
Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement Learning
Collaborative AI Teaming in Unknown Environments via Active Goal Deduction
SMAUG: A Sliding Multidimensional Task Window-Based MARL Framework for Adaptive Real-Time Subtask Recognition
Efficient Episodic Memory Utilization of Cooperative Multi-Agent Reinforcement Learning
Aligning Individual and Collective Objectives in Multi-Agent Cooperation
COA-GPT: Generative Pre-trained Transformers for Accelerated Course of Action Development in Military Operations
SwarmBrain: Embodied agent for real-time strategy game StarCraft II via large language models
BET: Explaining Deep Reinforcement Learning through The Error-Prone Decisions
StarCraftImage: A Dataset For Prototyping Spatial Reasoning Methods For Multi-Agent Environments
Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approach
DCIR: Dynamic Consistency Intrinsic Reward for Multi-Agent Reinforcement Learning
CODEX: A Cluster-Based Method for Explainable Reinforcement Learning
Minimax Exploiter: A Data Efficient Approach for Competitive Self-Play
JaxMARL: Multi-Agent RL Environments and Algorithms in JAX
Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization
Fidelity-Induced Interpretable Policy Extraction for Reinforcement Learning
Leveraging World Model Disentanglement in Value-Based Multi-Agent Reinforcement Learning
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