updated readmes

Author: vonHartz

CONTRIBUTING.md

@@ -1,28 +1,12 @@
 # Contributing
 
-## Proposal for vanilla installation
-
-Most dependencies even for the extras can just be automatically installed. However, there's two exceptions: ray (as we install from URL which PyPI does not allow) and tensorflow (as we need two different versions).
+## Developer Installation
 
-Installation from PyPI should work like this:
+For development, manual installation is the easiest way to stay up-to-date:
 ```bash
-pip install mdp_playground
-
-# if either extra is needed:
-pip install tensorflow==1.13.0rc1
-pip install ray[rllib,debug]==0.7.3
-pip install mdp_playground[extras_cont]
-
-# respectively:
-pip install tensorflow==2.2.0
-wget 'https://ray-wheels.s3-us-west-2.amazonaws.com/master/8d0c1b5e068853bf748f72b1e60ec99d240932c6/ray-0.9.0.dev0-cp36-cp36m-manylinux1_x86_64.whl'
-pip install ray-0.9.0.dev0-cp36-cp36m-manylinux1_x86_64.whl[rllib,debug]
-pip install mdp_playground[extras_disc]
-
+pip install -e .[extras]
 ```
 
-## Developer Installation
-
 In addition to the standard dependencies, please install the following:
 ```bash
 pip install sphinx, sphinx-book-theme  # for generating documentation
@@ -35,12 +19,14 @@ curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poet
 
 The `pyproject.toml` is set up using `poetry`.
 
-Build via `poetry build`, and publish via `poetry publish`.
+Build via `poetry build`, and publish via `poetry publish`. There are automatic workflows in place to build and publish on new package revisions.
+
+To enable manual installation with `poetry`, we also include a `setup.py` which needs to be kept up-to-date.
 
 
 ## Docs
 
-The documentation can be build using sphinx via:
+The documentation can be built using sphinx via:
 ```bash
 cd docs
 make html
@@ -56,7 +42,3 @@ To publish:
 ```bash
 git subtree push --prefix docs/_build/html/ origin gh-pages
 ```
-
-## WIP
-
-Add CI/CD for codecov etc.

README.md

@@ -32,7 +32,24 @@ There are 4 parts to the package:
 4) **Analysis**: [`plot_experiments.ipynb`](plot_experiments.ipynb) contains code to plot the standard plots from the paper.
 
 ## Installation
-### Manual
+
+### Production use
+We recommend using `conda` to manage environments. After setup of the environment, you can install MDP Playground in two ways:
+#### Manual
+To install MDP Playground manually, clone the repository and run:
+```bash
+pip install -e .[extras]
+```
+This might be the preferred way if you want easy access to the included experiments.
+
+#### From PyPI
+MDP Playground is also on PyPI. Just run:
+```bash
+pip install mdp_playground[extras]
+```
+
+
+### Reproducing results from the paper
 We recommend using `conda` environments to manage virtual `Python` environments to run the experiments. Unfortunately, you will have to maintain 2 environments - 1 for the "older" **discrete toy** experiments and 1 for the "newer" **continuous and complex** experiments from the paper. As mentioned in Appendix P in the paper, this is because of issues with Ray, the library that we used for our baseline agents.
 
 Please follow the following commands to install for the discrete toy experiments:
@@ -53,37 +70,14 @@ wget 'https://ray-wheels.s3-us-west-2.amazonaws.com/master/8d0c1b5e068853bf748f7
 pip install ray-0.9.0.dev0-cp36-cp36m-manylinux1_x86_64.whl[rllib,debug]
 ```
 
-### From PyPI
-MDP Playground is also on PyPI. As with the manual installation, we recommend using `conda` to manage environments. After setup of the environment, you can install MDP Playground like this:
-```bash
-# for production use:
-pip install tensorflow==2.2.0
-wget 'https://ray-wheels.s3-us-west-2.amazonaws.com/master/8d0c1b5e068853bf748f72b1e60ec99d240932c6/ray-0.9.0.dev0-cp36-cp36m-manylinux1_x86_64.whl'
-pip install ray-0.9.0.dev0-cp36-cp36m-manylinux1_x86_64.whl[rllib,debug]
-pip install mdp_playground
-
-# for the discrete toy experiments:
-pip install tensorflow==1.13.0rc1
-pip install ray[rllib,debug]==0.7.3
-pip install mdp_playground[extras_cont]
-
-# for the continuous and complex experiments:
-pip install tensorflow==2.2.0
-wget 'https://ray-wheels.s3-us-west-2.amazonaws.com/master/8d0c1b5e068853bf748f72b1e60ec99d240932c6/ray-0.9.0.dev0-cp36-cp36m-manylinux1_x86_64.whl'
-pip install ray-0.9.0.dev0-cp36-cp36m-manylinux1_x86_64.whl[rllib,debug]
-pip install mdp_playground[extras_disc]
-```
-
-Instead of calling `$ python run_experiments.py` in the cloned repository, you can then use `$ run-mdpp-experiments` from anywhere. Note, that you still need to specify the path to to the experiment's configuration file. Thus, for reproducing the experiments from the paper, the manual installation is the easier option.
-
 ## Running experiments
 For reproducing experiments from the main paper, please see [below](#running-experiments-from-the-main-paper).
 
 For general instructions, please continue reading.
 
 You can run experiments using:
 ```
-python run_experiments.py -c <config_file> -e <exp_name> -n <config_num>
+run-mdpp-experiments -c <config_file> -e <exp_name> -n <config_num>
 ```
 The `exp_name` is a prefix for the filenames of CSV files where stats for the experiments are recorded. The CSV stats files will be saved to the current directory.<br>
 Each of the command line arguments has defaults. Please refer to the documentation inside [`run_experiments.py`](run_experiments.py) for further details on the command line arguments. (Or run it with the `-h` flag to bring up help.)