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 enabled by passing tensorboard_dir=/path/to/log_dir.
dqn.fit(dataset.episodes, tensorboard_dir='runs')