The MADP Toolbox

An Open-Source library for planning and learning in (Multi-)Agent systems

Conference paper (2015)

Authors

F.A. Oliehoek University of Liverpool
M.T.J. Spaan Algorithmics - EEMCS
Philipp Robbel Massachusetts Institute of Technology
Joao Messias Universiteit van Amsterdam
Research Group
Algorithmics (EEMCS) (TU Delft)
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Published Date

01-01-2015

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Algorithmics

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.