How to get started

General steps

• Register, of course, for free to receive access to the login area.
• Start developing your algorithms and test that they run without errors and reasonably fast.
• During the training phase we perform nightly competition runs, you can access and see your performance results in the login area. There you can also upload new algorithms.
• Submit your final algorithms before the deadline for the ultimate competition simulation.

Code: duopoly.py with the price function p(.)

A simple example algorithm code with the explanation of the main functionalities can be found here.

The submitted algorithm file needs to be exactly called ‘duopoly.py’ and it needs to contain a pricing function p, which takes the following parameter. The function p(.) returns the tuple of (price, information_dump).

def p(
    current_selling_season: int,
    selling_period_in_current_season: int,
    prices_historical_in_current_season: Union[np.ndarray, None],
    demand_historical_in_current_season: Union[np.ndarray, None],
    competitor_has_capacity_current_period_in_current_season: bool,
    information_dump=Optional[Any],
) -> Tuple[float, Any]:

Code Testing

Cloud testing environment

In the login area under File Manager, you can upload your duopoly.py file alongside any other supplementary files you need. After the upload, we will start a small test run with your algorithms in the cloud. After approx. 15 minutes you can see the performance results with potential error logs for download in the platform.

Additional getting started information

An example notebook to start analysing the competition detail logs and how to simulate your algorithm locally can be found here.

Usage of duopoly_feedback.data

The DPC environment allows you to store feedback data in a dedicated file. You can use this file to pass your algorithm information across different competitions in the nightly simulation runs, as well as providing yourself information to download in the UI after the competition run. Here is an example to use pickle to store information in this file, but you can use any file format you like.

import pickle

# write any feedback object to file
with open('duopoly_feedback.data', 'wb') as handle:
            pickle.dump(FEEDBACK_OCJECT, handle, protocol=pickle.HIGHEST_PROTOCOL)

# load feedback object
with open('duopoly_feedback.data', 'rb') as handle:
            feedback = pickle.load(handle)

Note, the duopoly_feedback.data file is not persistent over multiple simulation days. Every nightly simulation run starts in a fresh container.

The duopoly_feedback.data file of the nightly simulation run is available for download in the detailed results section of the UI.

Example how to load additional submitted files

You may want to upload additional files, e.g. pickle files. In order to open these files in the competition container, please use the following code example. Your code location in the container is under /root/partcipant_folder/ and therefore please add this to the file location when loading the file.

import pickle

try:
    # for the cloud in container
    with open('/root/partcipant_folder/FILENAME.pkl', 'rb') as f:
        obj = pickle.load(f)
except:
    # for your local testing
    with open('FILENAME.pkl', 'rb') as f:
        obj = pickle.load(f)

Note, you don’t need to take care of this folder structure if you just want to import your custom scripts, simply import them. All your uploaded files will be located in the same folder within the competition container.

Also, the ‘duopoly_feedback.data' does not require this extra path handling. The feedback data object has a special setup to pass to yourself information for download in the DPC website, this file will be available in the website under ‘Download Feedback Data' in the results page.