39 papers with code • 16 benchmarks • 17 datasets

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


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Most implemented papers

The StarCraft Multi-Agent Challenge

oxwhirl/pymarl 11 Feb 2019

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

oxwhirl/pymarl ICML 2018

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 16 Jun 2017

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

Counterfactual Multi-Agent Policy Gradients

opendilab/DI-engine 24 May 2017

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

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

cyanrain7/trpo-in-marl 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.

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

mlr-org/mlrMBO 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.

MAVEN: Multi-Agent Variational Exploration

AnujMahajanOxf/MAVEN NeurIPS 2019

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

QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning

Sonkyunghwan/QTRAN 14 May 2019

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

huaifeng1993/DFANet CVPR 2019

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