Papers Starcraft
“Starcraft” 태그가 달린 논문 311편 · 필터 해제
Transformer World Model for Sample Efficient Multi-Agent Reinforcement Learning
We present the Multi-Agent Transformer World Model (MATWM), a novel transformer-based world model designed for multi-agent reinforcement learning in both vector- and image-based environments. MATWM combines a decentraliz…
Multi-agent Reinforcement LearningStarcraftA Benchmark for Generalizing Across Diverse Team Strategies in Competitive Pokémon
Developing AI agents that can robustly adapt to dramatically different strategic landscapes without retraining is a central challenge for multi-agent learning. Pok\'emon Video Game Championships (VGC) is a domain with an…
Large Language ModelStarcraftNeuroPAL: Punctuated Anytime Learning with Neuroevolution for Macromanagement in Starcraft: Brood War
StarCraft: Brood War remains a challenging benchmark for artificial intelligence research, particularly in the domain of macromanagement, where long-term strategic planning is required. Traditional approaches to StarCraf…
Computational EfficiencyStarcraftLanguage-Guided Multi-Agent Learning in Simulations: A Unified Framework and Evaluation
This paper introduces LLM-MARL, a unified framework that incorporates large language models (LLMs) into multi-agent reinforcement learning (MARL) to enhance coordination, communication, and generalization in simulated ga…
Language ModelingLanguage ModellingMulti-agent Reinforcement LearningStarcraft+2Dynamic Sight Range Selection in Multi-Agent Reinforcement Learning
Multi-agent reinforcement Learning (MARL) is often challenged by the sight range dilemma, where agents either receive insufficient or excessive information from their environment. In this paper, we propose a novel method…
Multi-agent Reinforcement Learningreinforcement-learningReinforcement LearningSMAC+2AVA: Attentive VLM Agent for Mastering StarCraft II
We introduce Attentive VLM Agent (AVA), a multimodal StarCraft II agent that aligns artificial agent perception with the human gameplay experience. Traditional frameworks such as SMAC rely on abstract state representatio…
Retrieval-augmented GenerationSMACSMAC+Starcraft+1SrSv: Integrating Sequential Rollouts with Sequential Value Estimation for Multi-agent Reinforcement Learning
Although multi-agent reinforcement learning (MARL) has shown its success across diverse domains, extending its application to large-scale real-world systems still faces significant challenges. Primarily, the high complex…
MuJoCoMulti-agent Reinforcement LearningStarcraftTrajectory-Class-Aware Multi-Agent Reinforcement Learning
In the context of multi-agent reinforcement learning, generalization is a challenge to solve various tasks that may require different joint policies or coordination without relying on policies specialized for each task. …
Multi-agent Reinforcement Learningreinforcement-learningReinforcement LearningStarcraft+2Nucleolus Credit Assignment for Effective Coalitions in Multi-agent Reinforcement Learning
In cooperative multi-agent reinforcement learning (MARL), agents typically form a single grand coalition based on credit assignment to tackle a composite task, often resulting in suboptimal performance. This paper propos…
Multi-agent Reinforcement LearningQ-LearningStarcraftPMAT: Optimizing Action Generation Order in Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) faces challenges in coordinating agents due to complex interdependencies within multi-agent systems. Most MARL algorithms use the simultaneous decision-making paradigm but ignore…
Action GenerationDecision MakingManagementMuJoCo+6Reflection of Episodes: Learning to Play Game from Expert and Self Experiences
StarCraft II is a complex and dynamic real-time strategy (RTS) game environment, which is very suitable for artificial intelligence and reinforcement learning research. To address the problem of Large Language Model(LLM)…
Language ModelingLanguage ModellingLarge Language ModelStarcraft+1Hierarchical Expert Prompt for Large-Language-Model: An Approach Defeat Elite AI in TextStarCraft II for the First Time
Since the emergence of the Large Language Model (LLM), LLM has been widely used in fields such as writing, translating, and searching. However, there is still great potential for LLM-based methods in handling complex tas…
Decision MakingLanguage ModelingLanguage ModellingLarge Language Model+2Cooperative Multi-Agent Planning with Adaptive Skill Synthesis
Despite much progress in training distributed artificial intelligence (AI), building cooperative multi-agent systems with multi-agent reinforcement learning (MARL) faces challenges in sample efficiency, interpretability,…
Decision MakingMulti-agent Reinforcement LearningStarcraftLow-Rank Agent-Specific Adaptation (LoRASA) for Multi-Agent Policy Learning
Multi-agent reinforcement learning (MARL) often relies on \emph{parameter sharing (PS)} to scale efficiently. However, purely shared policies can stifle each agent's unique specialization, reducing overall performance in…
MuJoCoMulti-agent Reinforcement LearningSMACSMAC++1Innovative activities of Activision Blizzard: A patent network analysis
Microsoft's acquisition of Activision Blizzard valued at $68.7 billion has drastically altered the landscape of the video game industry. At the time of the takeover, the intellectual properties of Activision Blizzard inc…
Patent classificationStarcraftSuperhuman Game AI Disclosure: Expertise and Context Moderate Effects on Trust and Fairness
As artificial intelligence surpasses human performance in select tasks, disclosing superhuman capabilities poses distinct challenges for fairness, accountability, and trust. However, the impact of such disclosures on div…
EthicsFairnessStarcraftStarcraft IITackling Uncertainties in Multi-Agent Reinforcement Learning through Integration of Agent Termination Dynamics
Multi-Agent Reinforcement Learning (MARL) has gained significant traction for solving complex real-world tasks, but the inherent stochasticity and uncertainty in these environments pose substantial challenges to efficien…
Distributional Reinforcement LearningMulti-agent Reinforcement Learningreinforcement-learningReinforcement Learning+2Human-like Bots for Tactical Shooters Using Compute-Efficient Sensors
Artificial intelligence (AI) has enabled agents to master complex video games, from first-person shooters like Counter-Strike to real-time strategy games such as StarCraft II and racing games like Gran Turismo. While the…
CPUImitation LearningReal-Time Strategy GamesStarcraft+1SMAC-Hard: Enabling Mixed Opponent Strategy Script and Self-play on SMAC
The availability of challenging simulation environments is pivotal for advancing the field of Multi-Agent Reinforcement Learning (MARL). In cooperative MARL settings, the StarCraft Multi-Agent Challenge (SMAC) has gained…
BenchmarkingMulti-agent Reinforcement LearningSMACSMAC++1Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning
Recently, deep Multi-Agent Reinforcement Learning (MARL) has demonstrated its potential to tackle complex cooperative tasks, pushing the boundaries of AI in collaborative environments. However, the efficiency of these sy…
DiversityMulti-agent Reinforcement LearningStarcraftStarcraft II