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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/uoe-agents/pressureplate

공식 코드

Assigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy Optimization

https://github.com/uoe-agents/smaclite

공식 코드

SMAClite: A Lightweight Environment for Multi-Agent Reinforcement Learning

https://github.com/vwxyzjn/gym-microrts

공식 코드

Gym-$μ$RTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning

https://github.com/vwxyzjn/gym-microrts-paper

공식 코드

Gym-$μ$RTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning

https://github.com/wendelinboehmer/dcg

공식 코드

Deep Coordination Graphs

https://github.com/wjh720/QPLEX

공식 코드

QPLEX: Duplex Dueling Multi-Agent Q-Learning

https://github.com/wudiymy/dqmix

공식 코드

MCMARL: Parameterizing Value Function via Mixture of Categorical Distributions for Multi-Agent Reinforcement Learning

https://github.com/wuhuikai/MSC

공식 코드

MSC: A Dataset for Macro-Management in StarCraft II

https://github.com/yiqinyang/icq

공식 코드

Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning

https://github.com/youpengzhao/crashed_agent

공식 코드

Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed Agents

https://github.com/ysmei97/mac-po

공식 코드

MAC-PO: Multi-Agent Experience Replay via Collective Priority Optimization

https://github.com/zhang-shaowei/smmae

공식 코드

Self-Motivated Multi-Agent Exploration

https://github.com/zhengyinan-air/omiga

공식 코드

Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization

https://github.com/zyh1999/cadp

공식 코드

Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?

https://github.com/1230113202/NV-JM-DD

The Natural Language of Actions

https://github.com/15534081591/QMIX

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

https://github.com/16444take/aope-sim

Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?

https://github.com/2023-MindSpore-1/ms-code-221/tree/main/qmix

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

https://github.com/2796gaurav/code_examples/tree/main/Perceiver

Perceiver IO: A General Architecture for Structured Inputs & Outputs

https://github.com/4rChon/NL-FuN

StarCraft II: A New Challenge for Reinforcement Learning

https://github.com/Coac/CommNet-BiCnet

Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games

https://github.com/DDoeuiGongju/MARL_project

RODE: Learning Roles to Decompose Multi-Agent Tasks

https://github.com/Denys88/rl_games

The StarCraft Multi-Agent Challenge

https://github.com/GaoFangshu/ELF-example

ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games

https://github.com/HanbumKo/distributed-wqmix

Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

https://github.com/Liberty3000/rl

Relational Deep Reinforcement Learning

https://github.com/MUmarJaved/MultiAgent-Distributed-Reinforcement-Learning

Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning

https://github.com/MindCode-4/code-2/tree/main/perceiver

Perceiver IO: A General Architecture for Structured Inputs & Outputs

https://github.com/MindSpore-scientific-2/code-7/tree/main/SISDTA

Perceiver IO: A General Architecture for Structured Inputs & Outputs

https://github.com/NervanaSystems/coach

StarCraft II: A New Challenge for Reinforcement Learning

https://github.com/NinaPi/Lunar_Causal_Network

Explainable Reinforcement Learning Through a Causal Lens

https://github.com/RL-WFU/IC3Net-Highway

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

https://github.com/SforAiDl/vformer

Perceiver IO: A General Architecture for Structured Inputs & Outputs

https://github.com/TeamSAIDA/SAIDA

Clear the Fog: Combat Value Assessment in Incomplete Information Games with Convolutional Encoder-Decoders

https://github.com/Teslatic/SC2-Freiburg

StarCraft II: A New Challenge for Reinforcement Learning

https://github.com/TestGenome/MSCS

MSC: A Dataset for Macro-Management in StarCraft II

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.