# SMAC   Edit

18 papers with code • 0 benchmarks • 0 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.

# 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.

1,497

# Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

19 Mar 2020

At the same time, it is often possible to train the agents in a centralised fashion where global state information is available and communication constraints are lifted.

847

# 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.

847

# 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.

179

# FACMAC: Factored Multi-Agent Centralised Policy Gradients

14 Mar 2020

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.

98

# 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.

86

# MAVEN: Multi-Agent Variational Exploration

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

86

# UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers

20 Jan 2021

Recent advances in multi-agent reinforcement learning have been largely limited in training one model from scratch for every new task.

49

# 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.

12

# 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.

12