SMAC+
27개 벤치마크 · 논문 126편 · 이 태스크의 논문 보기 →
Benchmarks
SMAC 6h_vs_8z
SMAC MMM2
SMAC 3s5z_vs_3s6z
SMAC corridor
SMAC 27m_vs_30m
Def_Armored_sequential
Def_Infantry_sequential
Def_Outnumbered_sequential
Def_Armored_parallel
Def_Infantry_parallel
Def_Outnumbered_parallel
Off_Complicated_parallel
Off_Distant_parallel
Off_Hard_parallel
Off_Near_parallel
Off_Superhard_parallel
SMAC 26m_vs_30m
SMAC 3s5z_vs_4s6z
SMAC 6h_vs_9z
SMAC MMM2_7m2M1M_vs_8m4M1M
SMAC MMM2_7m2M1M_vs_9m3M1M
SMAC corridor_2z_vs_24zg
Off_Distant_sequential
Off_Hard_sequential
Off_Near_sequential
Off_Superhard_sequential
Most implemented
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
The StarCraft Multi-Agent Challenge
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Value-Decomposition Networks For Cooperative Multi-Agent Learning
Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?
Counterfactual Multi-Agent Policy Gradients
Papers
Generalizable Agent Modeling for Agent Collaboration-Competition Adaptation with Multi-Retrieval and Dynamic Generation
Adapting a single agent to a new multi-agent system brings challenges, necessitating adjustments across various tasks, environments, and interactions with unknown teammates and opponents. Addressing this challenge is hig…
Multi-agent Reinforcement LearningSMACSMAC+Zero-Shot LearningCurriculum Learning With Counterfactual Group Relative Policy Advantage For Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) has achieved strong performance in cooperative adversarial tasks. However, most existing methods typically train agents against fixed opponent strategies and rely on such meta-st…
counterfactualMulti-agent Reinforcement LearningSMACSMAC+Ensemble-MIX: Enhancing Sample Efficiency in Multi-Agent RL Using Ensemble Methods
Multi-agent reinforcement learning (MARL) methods have achieved state-of-the-art results on a range of multi-agent tasks. Yet, MARL algorithms typically require significantly more environment interactions than their sing…
Ensemble LearningMulti-agent Reinforcement LearningSMACSMAC+Dynamic Sight Range Selection in Multi-Agent Reinforcement Learning
Multi-agent reinforcement Learning (MARL) is often challenged by the sight range dilemma, where agents either receive insufficient or excessive information from their environment. In this paper, we propose a novel method…
Multi-agent Reinforcement Learningreinforcement-learningReinforcement LearningSMAC+2POCAII: Parameter Optimization with Conscious Allocation using Iterative Intelligence
In this paper we propose for the first time the hyperparameter optimization (HPO) algorithm POCAII. POCAII differs from the Hyperband and Successive Halving literature by explicitly separating the search and evaluation p…
Hyperparameter OptimizationSMACSMAC+JaxRobotarium: Training and Deploying Multi-Robot Policies in 10 Minutes
Multi-agent reinforcement learning (MARL) has emerged as a promising solution for learning complex and scalable coordination behaviors in multi-robot systems. However, established MARL platforms (e.g., SMAC and MPE) lack…
BenchmarkingGPUMulti-agent Reinforcement LearningSMAC+1