Deep Learning Quick Reference

Training

Keras RL provides several Keras-like callbacks that allow for convenient model check pointing and logging. I'll use both of those callbacks below. If you would like to see more of the callbacks Keras-RL provides, they can be found here: https://github.com/matthiasplappert/keras-rl/blob/master/rl/callbacks.py. You can also find a Callback class that you can use to create your own Keras-RL callbacks.

We will use the following code to train our model:

def build_callbacks(env_name):    checkpoint_weights_filename = 'dqn_' + env_name + '_weights_{step}.h5f'    log_filename = 'dqn_{}_log.json'.format(env_name)    callbacks = [ModelIntervalCheckpoint(checkpoint_weights_filename, interval=5000)]    callbacks += [FileLogger(log_filename, interval=100 ...