Merge pull request #70 from jajimer/documentation

Documentation for Energym 1.1.0

doc/source/pages/installation.rst

@@ -51,18 +51,28 @@ Manual installation
 
 If you prefer installing *energym* manually, follow the steps below:
 
-1. Install EnergyPlus 8.6.0
+1. Install EnergyPlus 9.5.0
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Firstly, install EnergyPlus. Currently only version 8.6.0 has
-been tested, but code may also work with other versions.
+Firstly, install EnergyPlus. Currently it has been update compability to 9.5.0 and it has
+been tested, but code may also work with other versions. Energym ensure this support:
+
++------------------+--------------------+
+| Energym Version  | EnergyPlus version |
++==================+====================+
+| 1.0.0            | 8.6.0              | 
++------------------+--------------------+
+| 1.1.0            | 9.5.0              | 
++------------------+--------------------+
+
+Other combination may works, but they don't have been tested.
 
 Follow the instructions `here <https://energyplus.net/downloads>`__ and
 install it for Linux (only Ubuntu is supported). Choose any location
 to install the software. Once installed, a folder called
-``Energyplus-8-6-0`` should appear in the selected location.
+``Energyplus-9-5-0`` should appear in the selected location.
 
-2. Install BCVTB software
+1. Install BCVTB software
 ~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Follow the instructions

doc/source/pages/rewards.rst

@@ -18,7 +18,9 @@ Notice there are two temperature comfort ranges in that class, those ranges are
 two weights in the reward function, this allows you to adjust how important each aspect is when making a general evaluation of the environment.
 
 By default, all environments in gym register will use SimpleReward() with default parameters. However, this configuration can be overwriting in ``gym.make()``, for example:
-::
+
+.. code:: python
+    
     env = gym.make('Eplus-discrete-stochastic-mixed-v1', reward=ExpReward(energy_weight=0.5))
 
 .. note:: *Currently, it is only available these classes. However, more reward functions could be designed in the future!*

setup.py

@@ -4,7 +4,7 @@
     reqs = f.read().splitlines()
 
 setup(name='energym',
-      version='1.0.0',
+      version='1.1.0',
       install_requires=reqs,
       include_package_data=True,
       extras_require={