The MADP Toolbox: An Open-Source library for planning and learning in (Multi-)Agent systems

Frans A. Oliehoek, Matthijs T.J. Spaan, Philipp Robbel, Joao Messias

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)

Abstract

This article describes the MultiAgent Decision Process (MADP) toolbox, a software library to support planning and learning for intelligent agents and multiagent systems in uncertain environments. Some of its key features are that it supports partially observable environments and stochastic transition models; has unified support for single- and multiagent systems; provides a large number of models for decision- Theoretic decision making, including one-shot decision mak-ing (e.g., Bayesian games) and sequential decision making under various assumptions of observability and cooperation, such as Dec-POMDPs and POSGs; provides tools and parsers to quickly prototype new problems; provides an extensive range of planning and learning algorithms for single- and multiagent systems; and is written in C++ and designed to be extensible via the object-oriented paradigm.

Original languageEnglish
Title of host publicationSequential Decision Making for Intelligent Agents - Papers from the AAAI 2015 Fall Symposium, Technical Report
PublisherAI Access Foundation
Pages59-62
Number of pages4
VolumeFS-15-06
ISBN (Electronic)9781577357520
Publication statusPublished - 1 Jan 2015
EventAAAI 2015 Fall Symposium - Arlington, United States
Duration: 12 Nov 201514 Nov 2015

Conference

ConferenceAAAI 2015 Fall Symposium
CountryUnited States
CityArlington
Period12/11/1514/11/15

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