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Starcraft II

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하위 태스크: 0
평가표: 3
보존된 평가 기록: 3

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/esceptico/perceiver-io

Perceiver IO: A General Architecture for Structured Inputs & Outputs

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

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

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/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

https://github.com/karush17/emix

Energy-based Surprise Minimization for Multi-Agent Value Factorization

https://github.com/kcorder/qmix_variants

The StarCraft Multi-Agent Challenge

https://github.com/kered9/gym-microrts

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

https://github.com/kfu02/jaxmarl

JaxMARL: Multi-Agent RL Environments and Algorithms in JAX

https://github.com/kinalmehta/epymarl

The StarCraft Multi-Agent Challenge

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

Perceiver IO: A General Architecture for Structured Inputs & Outputs

https://github.com/lich14/CDS

Celebrating Diversity in Shared Multi-Agent Reinforcement Learning

https://github.com/ling-pan/res

The StarCraft Multi-Agent Challenge

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

Perceiver IO: A General Architecture for Structured Inputs & Outputs

https://github.com/mahi97/XQMIX

The StarCraft Multi-Agent Challenge

https://github.com/mavischer/DRRL

Relational Deep Reinforcement Learning

https://github.com/morning9393/HAPPO-HATRPO

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

https://github.com/nathangrinsztajn/Box-World

Relational Deep Reinforcement Learning

https://github.com/nathanin/pad

Relational Deep Reinforcement Learning

https://github.com/nicoladainese96/RelationalDeepRL

Relational Deep Reinforcement Learning

https://github.com/nicoladainese96/SC2-RL

StarCraft II: A New Challenge for Reinforcement Learning

https://github.com/nju-rl/acorm

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

https://github.com/opendilab/DI-engine

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

https://github.com/opendilab/DI-engine/blob/main/ding/policy/qmix.py

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

https://github.com/osilab-kaist/smac_exp

The StarCraft Multi-Agent Challenge

https://github.com/osilab-kaist/smac_plus

The StarCraft Multi-Agent Challenge

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