# SMAC+

30 papers with code • 16 benchmarks • 17 datasets

Bechmarks for Efficient Exploration of Completion of Multi-stage Tasks and Usage of Environmental Factors

## Libraries

Use these libraries to find SMAC+ models and implementations## Datasets

## 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

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.

# Counterfactual Multi-Agent Policy Gradients

COMA uses a centralised critic to estimate the Q-function and decentralised actors to optimise the agents' policies.

# Value-Decomposition Networks For Cooperative Multi-Agent Learning

We study the problem of cooperative multi-agent reinforcement learning with a single joint reward signal.

# 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.

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

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.

# 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.

# DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation

This paper introduces an extremely efficient CNN architecture named DFANet for semantic segmentation under resource constraints.

# MAVEN: Multi-Agent Variational Exploration

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