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Real-Time Strategy Games
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Real-Time Strategy (RTS) tasks involve training an agent to play video games with continuous gameplay and high-level macro-strategic goals such as map control, economic superiority and more.
<span style="color:grey; opacity: 0.6">( Image credit: Multi-platform Version of StarCraft: Brood War in a Docker Container )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
A Benchmark for Generalizing Across Diverse Team Strategies in Competitive Pokémon
NeuroPAL: Punctuated Anytime Learning with Neuroevolution for Macromanagement in Starcraft: Brood War
Language-Guided Multi-Agent Learning in Simulations: A Unified Framework and Evaluation
Dynamic Sight Range Selection in Multi-Agent Reinforcement Learning
AVA: Attentive VLM Agent for Mastering StarCraft II
Trajectory-Class-Aware Multi-Agent Reinforcement Learning
SrSv: Integrating Sequential Rollouts with Sequential Value Estimation for Multi-agent Reinforcement Learning
Nucleolus Credit Assignment for Effective Coalitions in 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
Cooperative Multi-Agent Planning with Adaptive Skill Synthesis
Low-Rank Agent-Specific Adaptation (LoRASA) for Multi-Agent Policy Learning
Innovative activities of Activision Blizzard: A patent network analysis
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
Three-dimensional attention Transformer for state evaluation in real-time strategy games
Human-like Bots for Tactical Shooters Using Compute-Efficient Sensors
SMAC-Hard: Enabling Mixed Opponent Strategy Script and Self-play on SMAC
Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning
LLM-PySC2: Starcraft II learning environment for Large Language Models
Data-Driven Distributed Common Operational Picture from Heterogeneous Platforms using Multi-Agent Reinforcement Learning
Offline-to-Online Multi-Agent Reinforcement Learning with Offline Value Function Memory and Sequential Exploration
A New Approach to Solving SMAC Task: Generating Decision Tree Code from Large Language Models
Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement Learning
Carefully Structured Compression: Efficiently Managing StarCraft II Data
Efficiently Scanning and Resampling Spatio-Temporal Tasks with Irregular Observations
Grounded Answers for Multi-agent Decision-making Problem through Generative World Model
ComaDICE: Offline Cooperative Multi-Agent Reinforcement Learning with Stationary Distribution Shift Regularization
Identifying and Clustering Counter Relationships of Team Compositions in PvP Games for Efficient Balance Analysis
On Stateful Value Factorization in Multi-Agent Reinforcement Learning
Hybrid Training for Enhanced Multi-task Generalization in Multi-agent Reinforcement Learning
CortexCompile: Harnessing Cortical-Inspired Architectures for Enhanced Multi-Agent NLP Code Synthesis
Assigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy Optimization
Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.