update the documentation

HISTORY.txt

+2014-03-11
+v4.0b1 - fist beta release created on the testing branch
+
 2013-09-11
 v4.0a4 - Package is useable with Python 3.3, used 2tp3 script to convert. Still
          needs a bit of cleaning up.

NEWS.txt

-2013-02-05
-The first alpha release of v4.0 will be tagged today, which indicates that the
-documentation and testing is starting to approach a level suitable for a final
-release. A 4.0 release date has not been set, but once there are unittests for
-all classes and functions, and the documentation is in a stable state, then it
-can be tagged.
-
-2013-01-26
-The release of v0.14 will be made today. With this all the functionality should
-be available to use. The initial version release of 4.0 will be made once the
-documentation has been finalised and unit tests have been written to cover all
-of the classes. The first release will be 4.0 to match the MATLAB, Octave, 
-Scilab and R MDP Toolboxes that are available at 
-http://www.inra.fr/mia/T/MDPtoolbox/

README.rst

@@ -6,6 +6,22 @@ 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 <http://www.mathworks.com/products/matlab/>`_ 
+`MDP toolbox http://www.inra.fr/mia/T/MDPtoolbox/>`_ by the
+`Biometry and Artificial Intelligence Unit <http://mia.toulouse.inra.fr/>`_ of
+`INRA Toulouse <http://www.toulouse.inra.fr/>`_ (France). There are editions
+available for MATLAB, GNU Octave, Scilab and R.
+
+Features
+--------
+  - Eight MDP algorithms implemented
+  - Fast array manipulation using `NumPy <http://www.numpy.org>`_
+  - Full sparse matrix support using
+    `SciPy's sparse package <http://www.scipy.org/SciPyPackages/Sparse>`_
+  - Optional linear programming support using
+    `cvxopt <http://abel.ee.ucla.edu/cvxopt/>`_
+
 Documentation
 -------------
 Documentation is available as docstrings in the module code and as html in the
@@ -22,7 +38,7 @@ Installation
        ``tar -xzvf pymdptoolbox-<VERSION>.tar.gz``
        ``unzip pymdptoolbox-<VERSION>``
 
-    3. Change to the MDP toolbox directory 
+    3. Change to the PyMDPtoolbox directory 
        ``cd pymdptoolbox``
 
     4. Install via Distutils, either to the filesystem or to a home directory
@@ -33,11 +49,24 @@ Quick Use
 ---------
 Start Python in your favourite way. Then follow the example below to import the
 module, set up an example Markov decision problem using a discount value of 0.9,
-and solve it using the value iteration algorithm.
+solve it using the value iteration algorithm, and then check the optimal policy.
 
-    >>> import mdptoolbox, mdptoolbox.example
+    >>> import mdptoolbox.example
     >>> P, R = mdptoolbox.example.forest()
     >>> vi = mdptoolbox.mdp.ValueIteration(P, R, 0.9)
+    >>> vi.run()
     >>> vi.policy
     (0, 0, 0)
 
+Contribute
+----------
+Issue Tracker: https://code.google.com/p/pymdptoolbox/issues/list
+Source Code: https://code.google.com/p/pymdptoolbox/source/checkout
+
+Support
+-------
+
+License
+-------
+The project is licensed under the BSD license. See LICENSE.txt for details.
+

TODO.txt

 1. Improve the documentation and rewrite it as neccessary.
 
-2. Work on converting the storage of the transitions and rewards as tuples of
-   arrays.
-
-3. Implement a nicer linnear programming interface to cvxopt, or write own
+2. Implement a nicer linnear programming interface to cvxopt, or write own
    linear programming code.
 
-4. Write unittests for all the classes.
-
-5. Implement own exception class.
-
-6. Move evalPolicy* functions to be module functions, as these are useful for
-   checking policies
+3. Write unittests for all the classes.
 
-7. Try to use rows for the Bellman computations rather than columns (perhaps
-   this storage is more Pythonic? or more NumPyic?)
+4. Implement own exception class.
 
-8. Create the mask as a sparse matrix in exampleRand 
+5. Move evalPolicy* functions to be functions of the util module, as these are
+   useful for checking policies in cases other than policy iteration algorithms.