Logging

d3rlpy algorithms automatically save model parameters and metrics under d3rlpy_logs directory.

from d3rlpy.datasets import get_cartpole
from d3rlpy.algos import DQN

dataset, env = get_cartpole()

dqn = DQN()

# metrics and parameters are saved in `d3rlpy_logs/DQN_YYYYMMDDHHmmss`
dqn.fit(dataset.episodes)

You can designate the directory.

# the directory will be `custom_logs/custom_YYYYMMDDHHmmss`
dqn.fit(dataset.episodes, logdir='custom_logs', experiment_name='custom')

If you want to disable all loggings, you can pass save_metrics=False.

dqn.fit(dataset.episodes, save_metrics=False)

TensorBoard

The same information is also automatically saved for tensorboard under runs directory. You can interactively visualize training metrics easily.

$ pip install tensorboard
$ tensorboard --logdir runs

This tensorboard logs can be disabled by passing tensorboard=False.

dqn.fit(dataset.episodes, tensorboard=False)