# SMAC

22 papers with code • 5 benchmarks • 1 datasets

The StarCraft Multi-Agent Challenge (SMAC) is a benchmark that provides elements of partial observability, challenging dynamics, and high-dimensional observation spaces. SMAC is built using the StarCraft II game engine, creating a testbed for research in cooperative MARL where each game unit is an independent RL agent.

## Libraries

Use these libraries to find SMAC models and implementations
2 papers
1,193
2 papers
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# The StarCraft Multi-Agent Challenge

11 Feb 2019

In this paper, we propose the StarCraft Multi-Agent Challenge (SMAC) as a benchmark problem to fill this gap.

16

# mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions

9 Mar 2017

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.

4

# Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?

18 Nov 2020

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.

4

# FACMAC: Factored Multi-Agent Centralised Policy Gradients

We propose FACtored Multi-Agent Centralised policy gradients (FACMAC), a new method for cooperative multi-agent reinforcement learning in both discrete and continuous action spaces.

3

# MAVEN: Multi-Agent Variational Exploration

We specifically focus on QMIX [40], the current state-of-the-art in this domain.

2

# Rethinking the Implementation Matters in Cooperative Multi-Agent Reinforcement Learning

6 Feb 2021

Multi-Agent Reinforcement Learning (MARL) has seen revolutionary breakthroughs with its successful application to multi-agent cooperative tasks such as computer games and robot swarms.

2

# Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF Surrogates

28 Jul 2016

Those methods adopt probabilistic surrogate models like Gaussian processes to approximate and minimize the validation error function of hyperparameter values.

1

# Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best

3 Jan 2019

This paper introduces a simple and fast variant of Planet Wars as a test-bed for statistical planning based Game AI agents, and for noisy hyper-parameter optimisation.

1

# On the Performance of Differential Evolution for Hyperparameter Tuning

15 Apr 2019

This empirical study involves a range of different machine learning algorithms and datasets with various characteristics to compare the performance of Differential Evolution with Sequential Model-based Algorithm Configuration (SMAC), a reference Bayesian Optimization approach.

1

# SMIX($λ$): Enhancing Centralized Value Functions for Cooperative Multi-Agent Reinforcement Learning

11 Nov 2019

Learning a stable and generalizable centralized value function (CVF) is a crucial but challenging task in multi-agent reinforcement learning (MARL), as it has to deal with the issue that the joint action space increases exponentially with the number of agents in such scenarios.

1