AI Arena Python Environment

To get started with our python environment you can run the training.py file.

This file shows you how to do a few things in our environment:

• Initialize a new model
• Import a pretrained model
• Set up the game environment
• Run training with one-sided and selfplay reinforcement learning
• Save your model in the format that works with our researcher platform

We have set you up with a starter model in the starter_model directory. This is a simple Policy Gradient that implements a version of the REINFORCE algorithm. We encourage you to replace this with your own models!

Additionally, we set up some basic training loops in the simulation_methods.py file. Feel free to change these up and make them your own!

NOTE: There are two variables in the training.py file which you should not change because our game requires these to be constant:

• n_features: This is the dimensionality of the state
• n_actions: This is the dimensionality of the policy

Lastly, we have included the rules-based agent agent_sihing.py (the researcher platform benchmark) in case you want to train specifically against it. But be careful about overfitting because we will introduce more benchmarks which require generalization...