paper-with-me

SMAC+ 벤치마크

SMAC+ on Off_Complicated_parallel

20개 결과 · ⬇ CSV · JSON

Median Win Rate

0 25 50 75 100 2017-05 2026-11 COMA — 0.0 (2017-05-24) COMA — 0.0 (2017-05-24) VDN — 70.0 (2017-06-16) VDN — 70.0 (2017-06-16) QMIX — 0.0 (2018-03-30) QMIX — 0.0 (2018-03-30) QTRAN — 0.0 (2019-05-14) QTRAN — 0.0 (2019-05-14) DDN — 0.0 (2021-02-16) DIQL — 0.0 (2021-02-16) DMIX — 0.0 (2021-02-16) DDN — 0.0 (2021-02-16) DIQL — 0.0 (2021-02-16) DMIX — 0.0 (2021-02-16) MASAC — 0.0 (2021-04-14) MASAC — 0.0 (2021-04-14) DRIMA — 100.0 (2021-09-29) DRIMA — 100.0 (2021-09-29) COMA — 0.0 (2017-05-24) VDN — 70.0 (2017-06-16) DRIMA — 100.0 (2021-09-29)
RankModel Median Win Rate PaperCodeYear
1 DRIMA 100 Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning 2021
2 VDN 70.0 Value-Decomposition Networks For Cooperative Multi-Agent Learning facebookresearch/benchmarl · tjuhaoxiaotian/pymarl3 · hhhusiyi-monash/UPDeT · +7 2017
3 IQL 35.0
4 MASAC 0.0 Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning puyuan1996/MARL 2021
4 COMA 0.0 Counterfactual Multi-Agent Policy Gradients opendilab/DI-engine · TonghanWang/NDQ · matteokarldonati/Counterfactual-Multi-Agent-Policy-Gradients · +4 2017
4 QTRAN 0.0 QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning opendilab/DI-engine · hhhusiyi-monash/UPDeT · Sonkyunghwan/QTRAN · +1 2019
4 QMIX 0.0 QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning ray-project/ray · opendilab/DI-engine · oxwhirl/pymarl · +15 2018
4 DDN 0.0 DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning j3soon/dfac 2021
4 DIQL 0.0 DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning j3soon/dfac 2021
4 DMIX 0.0 DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning j3soon/dfac 2021
11 DRIMA 100 Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning 2021
12 VDN 70.0 Value-Decomposition Networks For Cooperative Multi-Agent Learning facebookresearch/benchmarl · tjuhaoxiaotian/pymarl3 · hhhusiyi-monash/UPDeT · +7 2017
13 IQL 35.0
14 MASAC 0.0 Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning puyuan1996/MARL 2021
14 COMA 0.0 Counterfactual Multi-Agent Policy Gradients opendilab/DI-engine · TonghanWang/NDQ · matteokarldonati/Counterfactual-Multi-Agent-Policy-Gradients · +4 2017
14 QTRAN 0.0 QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning opendilab/DI-engine · hhhusiyi-monash/UPDeT · Sonkyunghwan/QTRAN · +1 2019
14 QMIX 0.0 QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning ray-project/ray · opendilab/DI-engine · oxwhirl/pymarl · +15 2018
14 DDN 0.0 DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning j3soon/dfac 2021
14 DIQL 0.0 DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning j3soon/dfac 2021
14 DMIX 0.0 DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning j3soon/dfac 2021
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