aiarena-gym 0.0.4

Gym environment for training agents in the AI Arena game

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...