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SMAC

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하위 태스크: 2
평가표: 11
보존된 평가 기록: 101

The StarCraft Multi-Agent Challenge (SMAC) is a benchmark that provides elements of partial observability, challenging dynamics, and high-dimensional observation spaces. SMAC is built using the StarCraft II game engine, creating a testbed for research in cooperative MARL where each game unit is an independent RL agent.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/AnujMahajanOxf/MAVEN

공식 코드

MAVEN: Multi-Agent Variational Exploration

https://github.com/Lamperougeyxy/GHQ

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GHQ: Grouped Hybrid Q Learning for Heterogeneous Cooperative Multi-agent Reinforcement Learning

https://github.com/PaLeroy/QVMix

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QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning

https://github.com/SimonLucas/ntbea

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Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best

https://github.com/Sonkyunghwan/QTRAN

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QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning

https://github.com/UTS-AAi/autoweka

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AutoWeka4MCPS-AVATAR: Accelerating Automated Machine Learning Pipeline Composition and Optimisation

https://github.com/WentseChen/Soft-QMIX

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Soft-QMIX: Integrating Maximum Entropy For Monotonic Value Function Factorization

https://github.com/ailabdsunipi/pymarlzooplus

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An Extended Benchmarking of Multi-Agent Reinforcement Learning Algorithms in Complex Fully Cooperative Tasks

https://github.com/camel-ai/vlm-play-starcraft2

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AVA: Attentive VLM Agent for Mastering StarCraft II

https://github.com/chaovven/SMIX

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SMIX($λ$): Enhancing Centralized Value Functions for Cooperative Multi-Agent Reinforcement Learning

https://github.com/cndota/aamas25-demar

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Dual Ensembled Multiagent Q-Learning with Hypernet Regularizer

https://github.com/colazhang22/pps-qmix

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PPS-QMIX: Periodically Parameter Sharing for Accelerating Convergence of Multi-Agent Reinforcement Learning

https://github.com/cugbbaiyun/hgcn-mix

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Cooperative Multi-Agent Reinforcement Learning with Hypergraph Convolution

https://github.com/devindeng94/llm-smac

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A New Approach to Solving SMAC Task: Generating Decision Tree Code from Large Language Models

https://github.com/devindeng94/smac-hard

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SMAC-Hard: Enabling Mixed Opponent Strategy Script and Self-play on SMAC

https://github.com/dig-beihang/ami

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Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence

https://github.com/gt-star-lab/jaxrobotarium

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JaxRobotarium: Training and Deploying Multi-Robot Policies in 10 Minutes

https://github.com/hhhusiyi-monash/UPDeT

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UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers

https://github.com/hijkzzz/pymarl2

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Rethinking the Implementation Matters in Cooperative Multi-Agent Reinforcement Learning

https://github.com/hsvgbkhgbv/shapley-q-learning

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SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning

https://github.com/hyeon1996/fox

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FoX: Formation-aware exploration in multi-agent reinforcement learning

https://github.com/j3soon/dfac

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DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning

https://github.com/j3soon/dfac-extended

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A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning

https://github.com/jbr-ai-labs/mamba

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Scalable Multi-Agent Model-Based Reinforcement Learning

https://github.com/jeffkinnison/shadho

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HomOpt: A Homotopy-Based Hyperparameter Optimization Method

https://github.com/junaiddk/transmix

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Transformer-based Value Function Decomposition for Cooperative Multi-agent Reinforcement Learning in StarCraft

https://github.com/kinalmehta/epymarl_spectral

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Effects of Spectral Normalization in Multi-agent Reinforcement Learning

https://github.com/linkjoker1006/pymarl3

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QTypeMix: Enhancing Multi-Agent Cooperative Strategies through Heterogeneous and Homogeneous Value Decomposition

https://github.com/liuqh16/mazero

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Efficient Multi-agent Reinforcement Learning by Planning

https://github.com/liushunyu/cia

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Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition

https://github.com/lucidrains/perceiver-pytorch

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Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World Models

https://github.com/lxxxxr/ices

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Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement Learning

https://github.com/maxc01/addtree

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Additive Tree-Structured Conditional Parameter Spaces in Bayesian Optimization: A Novel Covariance Function and a Fast Implementation

https://github.com/mlr-org/mlrMBO

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mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions

https://github.com/mooricAnna/HiSSD

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Learning Generalizable Skills from Offline Multi-Task Data for Multi-Agent Cooperation

https://github.com/navid-naderi/GraphMIX

공식 코드

Graph Convolutional Value Decomposition in Multi-Agent Reinforcement Learning

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