https://github.com/5had3z/sc2-experiments
공식 코드Carefully Structured Compression: Efficiently Managing StarCraft II Data
Papers with Code 아카이브
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>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MAS-anony/ASN
공식 코드Action Semantics Network: Considering the Effects of Actions in Multiagent Systems
https://github.com/Tencent/TStarBots
공식 코드TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game
https://github.com/Wei9711/GACG
공식 코드Group-Aware Coordination Graph for Multi-Agent Reinforcement Learning
https://github.com/Wei9711/LTSCG
공식 코드Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement Learning
https://github.com/ZifanWu/Coordinated-PPO
공식 코드https://github.com/aailab-kaist/trama
공식 코드https://github.com/ainfosec/codex
공식 코드CODEX: A Cluster-Based Method for Explainable Reinforcement Learning
https://github.com/cair/DeepRTS
공식 코드Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy Games
https://github.com/camel-ai/vlm-play-starcraft2
공식 코드https://github.com/carolinewang01/naht
공식 코드https://github.com/cathyhxh/ctds
공식 코드CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement Learning
https://github.com/cugbbaiyun/hgcn-mix
공식 코드Cooperative Multi-Agent Reinforcement Learning with Hypergraph Convolution
https://github.com/deepmind/deepmind-research/tree/master/perceiver
공식 코드Perceiver IO: A General Architecture for Structured Inputs & Outputs
https://github.com/deepmind/pysc2
공식 코드https://github.com/dig-beihang/ami
공식 코드Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence
https://github.com/flairox/jaxmarl
공식 코드https://github.com/hahayonghuming/VDACs
공식 코드https://github.com/hanhanAnderson/LSF-SAC
공식 코드https://github.com/hanhananderson/pac-marl
공식 코드https://github.com/hijkzzz/pymarl2
공식 코드Rethinking the Implementation Matters in Cooperative Multi-Agent Reinforcement Learning
https://github.com/histmeisah/large-language-models-play-starcraftii
공식 코드Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approach
https://github.com/hyeon1996/fox
공식 코드FoX: Formation-aware exploration in multi-agent reinforcement learning
https://github.com/hyunghona/emu
공식 코드Efficient Episodic Memory Utilization of Cooperative Multi-Agent Reinforcement Learning
https://github.com/jiwonjeon9603/maser
공식 코드MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer
https://github.com/jramapuram/variational_saccading
공식 코드Variational Saccading: Efficient Inference for Large Resolution Images
https://github.com/junaiddk/transmix
공식 코드https://github.com/kaszanas/sc2_datasets
공식 코드https://github.com/kkane99/MANGER_code
공식 코드Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning
https://github.com/kkane99/manger
공식 코드Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning
https://github.com/liuruoze/Raw-vs-Human-in-AlphaStar
공식 코드https://github.com/liuruoze/hiernet-sc2
공식 코드On Efficient Reinforcement Learning for Full-length Game of StarCraft II
https://github.com/liuruoze/mini-AlphaStar
공식 코드https://github.com/liuruoze/thought-sc2-model-experiment
공식 코드Efficient Reinforcement Learning for StarCraft by Abstract Forward Models and Transfer Learning
https://github.com/luchang1113/hep-llm-play-starcraftii
공식 코드https://github.com/marlbenchmark/on-policy
공식 코드The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.