The MADP Toolbox

The MADP Toolbox: An Open Source Library for Planning and Learning in (Multi-)Agent Systems

Oliehoek, F.A. (University of Liverpool)
Spaan, M.T.J. (TU Delft Algorithmics)
Terwijn, Bas (Universiteit van Amsterdam)
Robbel, Philipp (Massachusetts Institute of Technology)
Messias, João V. (Universiteit van Amsterdam)

2017-08

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. 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 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; it is released under a GNU GPL v3 license; and is written in C++ and designed to be extensible via the object-oriented paradigm.

software
decision-theoretic planning
reinforcement learning
multiagent systems

http://resolver.tudelft.nl/uuid:6537c2ef-f3b6-4e7d-89a0-b5a7a65bab43

1532-4435

Journal of Machine Learning Research, 18 (89), 1-5

Institutional Repository

journal article

© 2017 F.A. Oliehoek, M.T.J. Spaan, Bas Terwijn, Philipp Robbel, João V. Messias