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Starcraft

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Starcraft I is a RTS game; the task is to train an agent to play the game.

<span style="color:grey; opacity: 0.6">( Image credit: Macro Action Selection with Deep Reinforcement Learning in StarCraft )</span>

코드

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

https://github.com/29antonioac/StarCraft-winner-prediction

공식 코드

RedDwarfData: a simplified dataset of StarCraft matches

https://github.com/5had3z/sc2-experiments

공식 코드

Carefully Structured Compression: Efficiently Managing StarCraft II Data

https://github.com/Acciorocketships/pymarl2

공식 코드

QGNN: Value Function Factorisation with Graph Neural Networks

https://github.com/Bilibili/LastOrder

공식 코드

Macro action selection with deep reinforcement learning in StarCraft

https://github.com/CORE-Robotics-Lab/ProLoNets

공식 코드

Neural-encoding Human Experts' Domain Knowledge to Warm Start Reinforcement Learning

https://github.com/Games-and-Simulations/StarAlgo

공식 코드

StarAlgo: A Squad Movement Planning Library for StarCraft using Monte Carlo Tree Search and Negamax

https://github.com/Games-and-Simulations/sc-docker

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Multi-platform Version of StarCraft: Brood War in a Docker Container: Technical Report

https://github.com/IC3Net/IC3Net

공식 코드

Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks

https://github.com/Lamperougeyxy/GHQ

공식 코드

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

https://github.com/MAS-anony/ASN

공식 코드

Action Semantics Network: Considering the Effects of Actions in Multiagent Systems

https://github.com/PaLeroy/QVMix

공식 코드

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

https://github.com/TeamSAIDA/DefogGAN

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DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial Nets

https://github.com/Tencent/TStarBots

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TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game

https://github.com/TonghanWang/NDQ

공식 코드

Learning Nearly Decomposable Value Functions Via Communication Minimization

https://github.com/TorchCraft/StarData

공식 코드

STARDATA: A StarCraft AI Research Dataset

https://github.com/TorchCraft/TorchCraftAI

공식 코드

Growing Action Spaces

https://github.com/Wei9711/GACG

공식 코드

Group-Aware Coordination Graph for Multi-Agent Reinforcement Learning

https://github.com/Wei9711/LTSCG

공식 코드

Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement Learning

https://github.com/ZifanWu/Coordinated-PPO

공식 코드

Coordinated Proximal Policy Optimization

https://github.com/aailab-kaist/trama

공식 코드

Trajectory-Class-Aware Multi-Agent Reinforcement Learning

https://github.com/ainfosec/codex

공식 코드

CODEX: A Cluster-Based Method for Explainable Reinforcement Learning

https://github.com/awarelab/seed_rl

공식 코드

Off-Policy Correction For Multi-Agent Reinforcement Learning

https://github.com/azaddeihim/matwm

공식 코드

Transformer World Model for Sample Efficient Multi-Agent Reinforcement Learning

https://github.com/baicenxiao/arel

공식 코드

Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement Learning

https://github.com/cair/DeepRTS

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Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy Games

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

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AVA: Attentive VLM Agent for Mastering StarCraft II

https://github.com/cameronangliss/vgc-bench

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A Benchmark for Generalizing Across Diverse Team Strategies in Competitive Pokémon

https://github.com/carolinewang01/dm2

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DM$^2$: Decentralized Multi-Agent Reinforcement Learning for Distribution Matching

https://github.com/carolinewang01/naht

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N-Agent Ad Hoc Teamwork

https://github.com/cathyhxh/ctds

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CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement Learning

https://github.com/chaovven/SMIX

공식 코드

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

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/cranial-xix/marl-copa

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Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition

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

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Cooperative Multi-Agent Reinforcement Learning with Hypergraph Convolution

https://github.com/deepmind/deepmind-research/tree/master/perceiver

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Perceiver IO: A General Architecture for Structured Inputs & Outputs

https://github.com/deepmind/diplomacy

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Learning to Play No-Press Diplomacy with Best Response Policy Iteration

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