SMAC+
49 papers with code • 16 benchmarks • 17 datasets
Bechmarks for Efficient Exploration of Completion of Multi-stage Tasks and Usage of Environmental Factors
Benchmarks
These leaderboards are used to track progress in SMAC+
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Libraries
Use these libraries to find SMAC+ models and implementationsDatasets
SMAC-Exp
Def_Infantry_parallel
Def_Infantry_sequential
Def_Outnumbered_sequential
Def_Armored_sequential
Def_Outnumbered_parallel
Off_Hard_parallel
Off_Superhard_parallel
Def_Armored_parallel
Off_Near_parallel
Most implemented papers
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
We explore deep reinforcement learning methods for multi-agent domains.
The StarCraft Multi-Agent Challenge
oxwhirl/pymarl
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In this paper, we propose the StarCraft Multi-Agent Challenge (SMAC) as a benchmark problem to fill this gap.
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
At the same time, it is often possible to train the agents in a centralised fashion in a simulated or laboratory setting, where global state information is available and communication constraints are lifted.
Value-Decomposition Networks For Cooperative Multi-Agent Learning
facebookresearch/benchmarl
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We study the problem of cooperative multi-agent reinforcement learning with a single joint reward signal.
Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?
facebookresearch/benchmarl
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Most recently developed approaches to cooperative multi-agent reinforcement learning in the \emph{centralized training with decentralized execution} setting involve estimating a centralized, joint value function.
Counterfactual Multi-Agent Policy Gradients
COMA uses a centralised critic to estimate the Q-function and decentralised actors to optimise the agents' policies.
mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions
We present mlrMBO, a flexible and comprehensive R toolbox for model-based optimization (MBO), also known as Bayesian optimization, which addresses the problem of expensive black-box optimization by approximating the given objective function through a surrogate regression model.
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
We explore value-based solutions for multi-agent reinforcement learning (MARL) tasks in the centralized training with decentralized execution (CTDE) regime popularized recently.
MAVEN: Multi-Agent Variational Exploration
AnujMahajanOxf/MAVEN
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NeurIPS 2019
We specifically focus on QMIX [40], the current state-of-the-art in this domain.
DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation
huaifeng1993/DFANet
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CVPR 2019
This paper introduces an extremely efficient CNN architecture named DFANet for semantic segmentation under resource constraints.
