def save_summaries(metrics: Dict[str, float], writer: tf.summary.FileWriter,
global_step: int) -> None:
"""Log metrics with a tf.summary.FileWriter."""
values = [
tf.Summary.Value(tag=k, simple_value=v) for k, v in metrics.items()
]
summary = tf.Summary(value=values)
writer.add_summary(summary, global_step)
writer.flush()
def save_episode_to_summary(summary_writer: tensorflow.summary.FileWriter,
episode: int, step: int, time: float, reward: float, epsilon: float) -> None:
"""
Adds summary of episode to summary file
NOTE: to view summary execute "tensorboard --logdir output/[algorithm]/tensorboard_summary"
:param summary_writer: summary writer
:param episode: number of episode
:param step: total steps of episode
:param time: time needed to complete episode
:param reward: total reward received in episode
:param epsilon: value of epsilon at the end of episode
"""
# create summary of episode
summary = tensorflow.Summary()
summary.value.add(tag='Reward', simple_value=float(reward))
summary.value.add(tag='Step', simple_value=int(step))
summary.value.add(tag='Time', simple_value=float(time))
summary.value.add(tag='Epsilon', simple_value=float(epsilon))
# add summary to file writer
summary_writer.add_summary(summary, episode)
summary_writer.flush()
