Add more instructions to README.md

README.md

@@ -4,12 +4,50 @@
 This repository contains the source code and data for the experiments presented in
 _Deep Reinforcement Learning for Active Wake Control_.
 
-## How to replicate the experiments
+## How to Use This Repository
 
-Because each experiment has many parameters, we use configuration files to  describe each experiment. The configuration files for the experiments presented in the paper can be found in `./code/configs`: `action_representations_*.yml` for the action representation experiment (Section 4.1), and `noisy_*.yml` for the noisy observation experiment (Section 4.2). You can also create new configuration files to run your own experiments.
+### Replicating the Experiments
 
-To replicate the experiments in the paper, run `python3 code/main.py --config CONFIG_FILE`, where `CONFIG_FILE` is a path to a configuration file.
-The output data is written to a directory specified in the configuration, in Tensorboard format. You can use `./code/tensorboard_to_csv.py --path PATH` to convert the output from `PATH` to a `.csv`-file.
+Because each experiment has many parameters, we use configuration files to  describe each experiment. The configuration
+files for the experiments presented in the paper can be found in `./code/configs`: `action_representations_*.yml` for
+the action representation experiment (Section 4.1), and `noisy_*.yml` for the noisy observation experiment
+(Section 4.2). You can also create new configuration files to run your own experiments.
+
+Make sure that you are using Python 3.8 or newer. You also need to install required packages by running
+`python3 -m pip install -r requirements.txt` from the project's folder.
+
+To replicate the experiments in the paper, run `python3 code/main.py --config CONFIG_FILE`, where `CONFIG_FILE` is a
+path to a configuration file. The output data is written to a directory specified in the configuration, in Tensorboard
+format. You can use `./code/tensorboard_to_csv.py --path PATH` to convert the output from `PATH` to a `.csv`-file.
+
+### Building from the Source Code
+
+If you are interested in using the wind farm environment with your own reinforcement learning agents, you can build and
+install a Python package that will make the environment available on your machine.
+
+First, make sure that you have `build` installed by running `python3 -m pip install --upgrade build`.
+
+Next, build the package by running `python3 -m build` from the project's directory. This will create `./dist` with 
+a `wind_farm_gym-VERSION-py3-none-any.whl` file in it, where `VERSION` is the current release version.
+
+Finally, install the package by running `python3 -m pip install wind_farm_gym-VERSION-py3-none-any.whl`, or
+`python3 -m pip install wind_farm_gym-VERSION-py3-none-any.whl --force-reinstall` if you want to reinstall an existing
+installation. This should install the package and its dependencies.
+
+To test that the package is available and working, you can run the following script:
+```python
+from wind_farm_gym import WindFarmEnv
+
+# Initialize the environment with 3 turbines positioned 750 meters apart in a line
+env = WindFarmEnv(turbine_layout=([0, 750, 1500], [0, 0, 0]))
+
+obs = env.reset()
+for _ in range(1000):                # Repeat for 1000 steps
+    a = env.action_space.sample()    # Choose an action randomly
+    obs, reward, _, _ = env.step(a)  # Perform the action
+    env.render()                     # Render the environment; remove this line to speed up the process
+env.close()
+```
 
 ## Contents
 
@@ -18,10 +56,12 @@ The output data is written to a directory specified in the configuration, in Ten
     - `config` for configuration files used in the experiments;
     - `wind_farm_gym` for the environment.
 - `./data` contains the datasets used in the paper:
-    - `wind_data` contains the measurements from Hollandse Kust Noord (site B) used to estimate the transition model in Section 3.4.
+    - `wind_data` contains the measurements from Hollandse Kust Noord (site B) used to estimate the transition model in
+Section 3.4.
     - the other subdirectories contain the results of the experiments:
         - `action_representations_tunnel` for the experiment presented in Section 4.1.
-        - `noisy_1` .. `noisy_7` for the experiment presented in Section 4.2; each folder corresponds to a different level of noise, 1% .. 7%.
+        - `noisy_1` ... `noisy_7` for the experiment presented in Section 4.2; each folder corresponds to a different
+level of noise, 1% ... 7%.
 
 ## Citation