Papers with Code 아카이브
Starcraft II
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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/zhengyinan-air/omiga
공식 코드Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization
https://github.com/zyh1999/cadp
공식 코드Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?
https://github.com/1230113202/NV-JM-DD
https://github.com/15534081591/QMIX
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
https://github.com/16444take/aope-sim
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
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
https://github.com/Acciorocketships/pymarl2
Rethinking the Implementation Matters in Cooperative Multi-Agent Reinforcement Learning
https://github.com/DDoeuiGongju/MARL_project
https://github.com/Denys88/rl_games
https://github.com/Lamperougeyxy/GHQ
https://github.com/Liberty3000/rl
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
https://github.com/NinaPi/Lunar_Causal_Network
https://github.com/SforAiDl/vformer
Perceiver IO: A General Architecture for Structured Inputs & Outputs
https://github.com/Teslatic/SC2-Freiburg
https://github.com/TestGenome/MSCS
https://github.com/TonghanWang/DOP
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
https://github.com/TonghanWang/NDQ
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
https://github.com/TonghanWang/RODE
https://github.com/ailabdsunipi/pymarlzooplus
https://github.com/anonymous-iclr22/trust-region-in-multi-agent-reinforcement-learning
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
https://github.com/carolinewang01/dm2
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
https://github.com/chandar-lab/coe
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven Exploration
https://github.com/chauncygu/multi-agent-constrained-policy-optimisation
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
https://github.com/code-cultivater/MAPPG
https://github.com/cyanrain7/trpo-in-marl
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
https://github.com/cyanrain7/trust-region-policy-optimisation-in-multi-agent-reinforcement-learning
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
https://github.com/deepmind/alphastar
AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning
https://github.com/dtabas/epymarl
https://github.com/emerge-lab/nocturne_lab
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
https://github.com/ericborn/binarybot
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.