Markov Decision Process (MDP) Toolbox

pip install pymdptoolbox==4.0a2


Markov Decision Process (MDP) Toolbox for Python

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The MDP toolbox provides classes and functions for the resolution of descrete-time Markov Decision Processes. The list of algorithms that have been implemented includes backwards induction, linear programming, policy iteration, q-learning and value iteration along with several variations.

The classes and functions were developped based on the MATLAB MDP toolbox by the Biometry and Artificial Intelligence Unit of INRA Toulouse (France). There are editions available for MATLAB, GNU Octave, Scilab and R. The suite of MDP toolboxes are described in Chades I, Chapron G, Cros M-J, Garcia F & Sabbadin R (2014) 'MDPtoolbox: a multi-platform toolbox to solve stochastic dynamic programming problems', Ecography, vol. 37, no. 9, pp. 916–920, doi 10.1111/ecog.00888.


  • Eight MDP algorithms implemented
  • Fast array manipulation using NumPy
  • Full sparse matrix support using SciPy's sparse package
  • Optional linear programming support using cvxopt

PLEASE NOTE: the linear programming algorithm is currently unavailable except for testing purposes due to incorrect behaviour.


NumPy and SciPy must be on your system to use this toolbox. Please have a look at their documentation to get them installed. If you are installing onto Ubuntu or Debian and using Python 2 then this will pull in all the dependencies:

sudo apt-get install python-numpy python-scipy python-cvxopt

On the other hand, if you are using Python 3 then cvxopt will have to be compiled (pip will do it automatically). To get NumPy, SciPy and all the dependencies to have a fully featured cvxopt then run:

sudo apt-get install python3-numpy python3-scipy liblapack-dev libatlas-base-dev libgsl0-dev fftw-dev libglpk-dev libdsdp-dev

The two main ways of downloading the package is either from the Python Package Index or from GitHub. Both of these are explained below.

Python Package Index (PyPI)

Downloads Latest Version Development Status Wheel Status Egg Status Download format

The toolbox's PyPI page is and there are both zip and tar.gz archive options available that can be downloaded. However, I recommend using pip to install the toolbox if you have it available. Just type

pip install pymdptoolbox

at the console and it should take care of downloading and installing everything for you. If you also want cvxopt to be automatically downloaded and installed so that you can help test the linear programming algorithm then type

pip install "pymdptoolbox[LP]"

If you want it to be installed just for you rather than system wide then do

pip install --user pymdptoolbox

If you downloaded the package manually from PyPI

  1. Extract the *.zip or *.tar.gz archive

    tar -xzvf pymdptoolbox-<VERSION>.tar.gz

    unzip pymdptoolbox-<VERSION>

  2. Change to the PyMDPtoolbox directory

    cd pymdptoolbox

  3. Install via Setuptools, either to the root filesystem or to your home directory if you don't have administrative access.

    python install

    python install --user

    Read the Setuptools documentation for more advanced information.

Of course you can also use virtualenv or simply just unpack it to your working directory.


Clone the Git repository

git clone

and then follow from step two above. To learn how to use Git then I reccomend reading the freely available Pro Git book written by Scott Chacon and Ben Straub and published by Apress.

Quick Use

Start Python in your favourite way. The following example shows you how to import the module, set up an example Markov decision problem using a discount value of 0.9, solve it using the value iteration algorithm, and then check the optimal policy.

import mdptoolbox.example
P, R = mdptoolbox.example.forest()
vi = mdptoolbox.mdp.ValueIteration(P, R, 0.9)
vi.policy # result is (0, 0, 0)


Documentation is available at and also as docstrings in the module code. If you use IPython to work with the toolbox, then you can view the docstrings by using a question mark ?. For example:

import mdptoolbox

will display the relevant documentation.


Issue Tracker:

Source Code:


Use the issue tracker.


The project is licensed under the BSD license. See LICENSE.txt for details.