Markov Decision Process (MDP) Toolbox¶

The MDP toolbox provides classes and functions for the resolution of descrete-time Markov Decision Processes.

Available modules¶

example: Examples of transition and reward matrices that form valid MDPs
mdp: Makov decision process algorithms
util: Functions for validating and working with an MDP

How to use the documentation¶

Documentation is available both as docstrings provided with the code and in html or pdf format from The MDP toolbox homepage. The docstring examples assume that the mdptoolbox package is imported like so:

>>> import mdptoolbox

To use the built-in examples, then the example module must be imported:

>>> import mdptoolbox.example

Once the example module has been imported, then it is no longer neccesary to issue import mdptoolbox.

Code snippets are indicated by three greater-than signs:

>>> x = 17
>>> x = x + 1
>>> x
18

The documentation can be displayed with IPython. For example, to view the docstring of the ValueIteration class use mdp.ValueIteration?<ENTER>, and to view its source code use mdp.ValueIteration??<ENTER>.

Markov Decision Process (MDP) Toolbox¶

Available modules¶

How to use the documentation¶

Acknowledgments¶