Merge pull request #114 from intelligent-environments-lab/gymnasium-m…

…igration

Gymnasium migration: docs requirements and text refactoring

README.md

@@ -1,5 +1,5 @@
 # CityLearn
-CityLearn is an open source OpenAI Gym environment for the implementation of Multi-Agent Reinforcement Learning (RL) for building energy coordination and demand response in cities. A major challenge for RL in demand response is the ability to compare algorithm performance. Thus, CityLearn facilitates and standardizes the evaluation of RL agents such that different algorithms can be easily compared with each other.
+CityLearn is an open source Farama Foundation Gymnasium environment for the implementation of Multi-Agent Reinforcement Learning (RL) for building energy coordination and demand response in cities. A major challenge for RL in demand response is the ability to compare algorithm performance. Thus, CityLearn facilitates and standardizes the evaluation of RL agents such that different algorithms can be easily compared with each other.
 
 ![Demand-response](https://github.com/intelligent-environments-lab/CityLearn/blob/master/assets/images/dr.jpg)
 

citylearn/__main__.py

@@ -23,7 +23,7 @@ def __learn(**kwargs):
 
 def main():
     parser = argparse.ArgumentParser(prog='citylearn', formatter_class=argparse.ArgumentDefaultsHelpFormatter, description=('''
-        An open source OpenAI Gym environment for the implementation of Multi-Agent Reinforcement 
+        An open source Farama Foundation Gymnasium environment for the implementation of Multi-Agent Reinforcement 
         Learning (RL) for building energy coordination and demand response in cities.'''
     ))
     parser.add_argument('--version', action='version', version='%(prog)s' + f' {__version__}')

docs/requirements.txt

@@ -1,7 +1,7 @@
-jinja2==3.0.0
+jinja2
 myst-nb
 nbsphinx-link
-Sphinx==4.5.0
+Sphinx
 sphinxemoji
 sphinxcontrib-bibtex
 sphinx-copybutton

docs/source/index.rst

@@ -22,7 +22,7 @@ CityLearn
       :scale: 8 %
       :target: https://sdgs.un.org/goals/goal13
 
-CityLearn is an open source OpenAI Gym environment for the implementation of Multi-Agent Reinforcement Learning (RL) for building energy coordination and demand response in cities :cite:p:`https://doi.org/10.48550/arxiv.2012.10504, 10.1145/3360322.3360998`. A major challenge for RL in demand response is the ability to compare algorithm performance :cite:p:`VAZQUEZCANTELI20191072`. Thus, CityLearn facilitates and standardizes the evaluation of RL agents such that different algorithms can be easily compared with each other.
+CityLearn is an open source Farama Foundation Gymnasium environment for the implementation of Multi-Agent Reinforcement Learning (RL) for building energy coordination and demand response in cities :cite:p:`https://doi.org/10.48550/arxiv.2012.10504, 10.1145/3360322.3360998`. A major challenge for RL in demand response is the ability to compare algorithm performance :cite:p:`VAZQUEZCANTELI20191072`. Thus, CityLearn facilitates and standardizes the evaluation of RL agents such that different algorithms can be easily compared with each other.
 
 .. image:: ../../assets/images/dr.jpg
    :scale: 30 %

setup.py

@@ -22,7 +22,7 @@ def get_version():
     version=get_version(),
     author='Jose Ramon Vazquez-Canteli, Kingsley Nweye, Zoltan Nagy',
     author_email='[email protected]',
-    description='An open source OpenAI Gym environment for the implementation of Multi-Agent Reinforcement Learning (RL) for building energy coordination and demand response in cities.',
+    description='An open source Farama Foundation Gymnasium environment for the implementation of Multi-Agent Reinforcement Learning (RL) for building energy coordination and demand response in cities.',
     long_description=long_description,
     long_description_content_type='text/markdown',
     url='https://github.com/intelligent-environments-lab/CityLearn',