Multi-agent Reinforcement Learning on Def_Infantry_parallel

Metric: Median Win Rate (higher is better)

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#	Model↕	Median Win Rate▼	Augmentations	Paper	Date↕	Code
1	QTRAN	100	No	QTRAN: Learning to Factorize with Transformation...	2019-05-14	Code
2	DRIMA	100	No	-	-	-
3	QMIX	95	No	QMIX: Monotonic Value Function Factorisation for...	2018-03-30	Code
4	VDN	95	No	Value-Decomposition Networks For Cooperative Mul...	2017-06-16	Code
5	DMIX	90	No	DFAC Framework: Factorizing the Value Function v...	2021-02-16	Code
6	COMA	50	No	Counterfactual Multi-Agent Policy Gradients	2017-05-24	Code
7	DIQL	45	No	DFANet: Deep Feature Aggregation for Real-Time S...	2019-04-03	Code
8	IQL	40	No	The StarCraft Multi-Agent Challenges+ : Learning...	2022-07-05	Code
9	MASAC	30	No	Decomposed Soft Actor-Critic Method for Cooperat...	2021-04-14	Code
10	DDN	20	No	DFAC Framework: Factorizing the Value Function v...	2021-02-16	Code

#1QTRANSOTA
100
Median Win Rate· 2019-05-14
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning Code
#2DRIMA
100
Median Win Rate
No paper
#3QMIX
95
Median Win Rate· 2018-03-30
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning Code
#4VDNSOTA
95
Median Win Rate· 2017-06-16
Value-Decomposition Networks For Cooperative Multi-Agent Learning Code
#5DMIX
90
Median Win Rate· 2021-02-16
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning Code
#6COMASOTA
50
Median Win Rate· 2017-05-24
Counterfactual Multi-Agent Policy Gradients Code
#7DIQL
45
Median Win Rate· 2019-04-03
DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation Code
#8IQL
40
Median Win Rate· 2022-07-05
The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functions Code
#9MASAC
30
Median Win Rate· 2021-04-14
Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning Code
#10DDN
20
Median Win Rate· 2021-02-16
DFAC Framework: Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning Code