How to

Adversarial Imitation Learning (with Gaussian Process Forward Model)

To work with/run the code install the requirements in "requirements.txt" i.e.

pip install -r requirements.txt

A list of available arguments for the cart-pole learning task can be accessed through

python -m cartpole.learn -h

To run the cart-pole learning task do

python -m cartpole.learn [args]

For example, to run using the pathwise-gradient approach, a time horizon of $T=10$ and using a convolutional discriminator, do

python -m cartpole.learn --T=10 --use_pathwise_grad --use_conv_disc

Various plots and training data are automatically saved in a folder "cartpole/results/result-[datetime]".

The code for this part of the project can be found on the branch bayes_lin

The two main files there are bayesian_linear_regression.py and bayesian_logistic_regression.py. These can be ran using

python [filename]