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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/TonghanWang/DOP

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

https://github.com/TonghanWang/RODE

RODE: Learning Roles to Decompose Multi-Agent Tasks

https://github.com/TorchCraft/TorchCraft

TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games

https://github.com/ailabdsunipi/pymarlzooplus

The StarCraft Multi-Agent Challenge

https://github.com/anonymous-iclr22/trust-region-in-multi-agent-reinforcement-learning

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

https://github.com/anonymous1234517/code

Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning

https://github.com/apsdehal/gym-starcraft

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

https://github.com/chandar-lab/coe

Episodic Multi-agent Reinforcement Learning with Curiosity-Driven Exploration

https://github.com/chauncygu/multi-agent-constrained-policy-optimisation

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

https://github.com/code-cultivater/MAPPG

Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy Gradient

https://github.com/cts198859/deeprl_dist

Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning

https://github.com/cts198859/deeprl_network

Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning

https://github.com/cyanrain7/trpo-in-marl

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

https://github.com/cyanrain7/trust-region-policy-optimisation-in-multi-agent-reinforcement-learning

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

https://github.com/deepmind/alphastar

AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning

https://github.com/dongchen06/macacc

Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning

https://github.com/dtabas/epymarl

The StarCraft Multi-Agent Challenge

https://github.com/emerge-lab/nocturne_lab

The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games

https://github.com/ericborn/binarybot

StarCraft II: A New Challenge for Reinforcement Learning

https://github.com/esceptico/perceiver-io

Perceiver IO: A General Architecture for Structured Inputs & Outputs

https://github.com/facebookarchive/MazeBase

MazeBase: A Sandbox for Learning from Games

https://github.com/facebookresearch/benchmarl

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

https://github.com/farama-foundation/microrts-py

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

https://github.com/gingkg/smac

Counterfactual Multi-Agent Policy Gradients

https://github.com/goodbyeearth/myVBC

Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control

https://github.com/google-deepmind/pysc2

StarCraft II: A New Challenge for Reinforcement Learning

https://github.com/hcch0912/gym_wrapper_dm_soccer

Arena: a toolkit for Multi-Agent Reinforcement Learning

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

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

https://github.com/huggingface/transformers

Perceiver IO: A General Architecture for Structured Inputs & Outputs

https://github.com/ifpen/wfcrl-benchmark

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

https://github.com/inoryy/reaver

StarCraft II: A New Challenge for Reinforcement Learning

https://github.com/isp1tze/MAProj

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

https://github.com/jk96491/C-COMA

The StarCraft Multi-Agent Challenge

https://github.com/jk96491/SMAC

The StarCraft Multi-Agent Challenge

https://github.com/jugg1er/air

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

https://github.com/kachayev/gym-microrts-paper-sb3

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

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