Papers Starcraft
“Starcraft” 태그가 달린 논문 311편 · 필터 해제
Data-Driven Distributed Common Operational Picture from Heterogeneous Platforms using Multi-Agent Reinforcement Learning
The integration of unmanned platforms equipped with advanced sensors promises to enhance situational awareness and mitigate the "fog of war" in military operations. However, managing the vast influx of data from these pl…
Deep Reinforcement LearningMulti-agent Reinforcement LearningStarcraftLLM-PySC2: Starcraft II learning environment for Large Language Models
This paper introduces a new environment LLM-PySC2 (the Large Language Model StarCraft II Learning Environment), a platform derived from DeepMind's StarCraft II Learning Environment that serves to develop Large Language M…
Decision MakingLanguage ModellingLarge Language ModelSMAC+3Offline-to-Online Multi-Agent Reinforcement Learning with Offline Value Function Memory and Sequential Exploration
Offline-to-Online Reinforcement Learning has emerged as a powerful paradigm, leveraging offline data for initialization and online fine-tuning to enhance both sample efficiency and performance. However, most existing res…
Efficient ExplorationMulti-agent Reinforcement Learningreinforcement-learningReinforcement Learning+3A New Approach to Solving SMAC Task: Generating Decision Tree Code from Large Language Models
StarCraft Multi-Agent Challenge (SMAC) is one of the most commonly used experimental environments in multi-agent reinforcement learning (MARL), where the specific task is to control a set number of allied units to defeat…
Decision MakingMulti-agent Reinforcement LearningSMACSMAC++1Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement Learning
In multi-agent reinforcement learning (MARL), parameter sharing is commonly employed to enhance sample efficiency. However, the popular approach of full parameter sharing often leads to homogeneous policies among agents,…
DiversityMuJoCoMulti-agent Reinforcement Learningreinforcement-learning+2Efficiently Scanning and Resampling Spatio-Temporal Tasks with Irregular Observations
Various works have aimed at combining the inference efficiency of recurrent models and training parallelism of multi-head attention for sequence modeling. However, most of these works focus on tasks with fixed-dimension …
Language ModelingLanguage ModellingStarcraftStarcraft IICarefully Structured Compression: Efficiently Managing StarCraft II Data
Creation and storage of datasets are often overlooked input costs in machine learning, as many datasets are simple image label pairs or plain text. However, datasets with more complex structures, such as those from the r…
StarcraftStarcraft IIGrounded Answers for Multi-agent Decision-making Problem through Generative World Model
Recent progress in generative models has stimulated significant innovations in many fields, such as image generation and chatbots. Despite their success, these models often produce sketchy and misleading solutions for co…
Decision MakingImage GenerationMulti-agent Reinforcement LearningStarcraftComaDICE: Offline Cooperative Multi-Agent Reinforcement Learning with Stationary Distribution Shift Regularization
Offline reinforcement learning (RL) has garnered significant attention for its ability to learn effective policies from pre-collected datasets without the need for further environmental interactions. While promising resu…
MuJoCoMulti-agent Reinforcement LearningOffline RLreinforcement-learning+4On Stateful Value Factorization in Multi-Agent Reinforcement Learning
Value factorization is a popular paradigm for designing scalable multi-agent reinforcement learning algorithms. However, current factorization methods make choices without full justification that may limit their performa…
Multi-agent Reinforcement Learningreinforcement-learningReinforcement LearningStarcraft+1Hybrid Training for Enhanced Multi-task Generalization in Multi-agent Reinforcement Learning
In multi-agent reinforcement learning (MARL), achieving multi-task generalization to diverse agents and objectives presents significant challenges. Existing online MARL algorithms primarily focus on single-task performan…
Multi-agent Reinforcement LearningStarcraftAssigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy Optimization
Multi-agent proximal policy optimization (MAPPO) has recently demonstrated state-of-the-art performance on challenging multi-agent reinforcement learning tasks. However, MAPPO still struggles with the credit assignment p…
Multi-agent Reinforcement Learningreinforcement-learningReinforcement LearningStarcraft+1Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World Models
Learning a world model for model-free Reinforcement Learning (RL) agents can significantly improve the sample efficiency by learning policies in imagination. However, building a world model for Multi-Agent RL (MARL) can …
Reinforcement Learning (RL)SMACSMAC+StarcraftTree Search for Simultaneous Move Games via Equilibrium Approximation
Neural network supported tree-search has shown strong results in a variety of perfect information multi-agent tasks. However, the performance of these methods on partial information games has generally been below competi…
StarcraftIndividual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement Learning
In multi-agent reinforcement learning (MARL), effective exploration is critical, especially in sparse reward environments. Although introducing global intrinsic rewards can foster exploration in such settings, it often c…
Multi-agent Reinforcement LearningSMACSMAC+StarcraftVariational Offline Multi-agent Skill Discovery
Skills are effective temporal abstractions established for sequential decision making, which enable efficient hierarchical learning for long-horizon tasks and facilitate multi-task learning through their transferability.…
Decision MakingMulti-agent Reinforcement LearningMulti-Task LearningSequential Decision Making+1POWQMIX: Weighted Value Factorization with Potentially Optimal Joint Actions Recognition for Cooperative Multi-Agent Reinforcement Learning
Value function factorization methods are commonly used in cooperative multi-agent reinforcement learning, with QMIX receiving significant attention. Many QMIX-based methods introduce monotonicity constraints between the …
Multi-agent Reinforcement Learningreinforcement-learningReinforcement LearningStarcraft+1Group-Aware Coordination Graph for Multi-Agent Reinforcement Learning
Cooperative Multi-Agent Reinforcement Learning (MARL) necessitates seamless collaboration among agents, often represented by an underlying relation graph. Existing methods for learning this graph primarily focus on agent…
Decision MakingMulti-agent Reinforcement Learningreinforcement-learningReinforcement Learning+2N-Agent Ad Hoc Teamwork
Current approaches to learning cooperative multi-agent behaviors assume relatively restrictive settings. In standard fully cooperative multi-agent reinforcement learning, the learning algorithm controls $\textit{all}$ ag…
Autonomous DrivingMulti-agent Reinforcement LearningOut-of-Distribution Generalizationreinforcement-learning+3Heterogeneous Multi-Agent Reinforcement Learning for Zero-Shot Scalable Collaboration
The emergence of multi-agent reinforcement learning (MARL) is significantly transforming various fields like autonomous vehicle networks. However, real-world multi-agent systems typically contain multiple roles, and the …
Multi-agent Reinforcement Learningreinforcement-learningReinforcement LearningSMAC+2