def load_csv(self, csv_dataset: CsvDataset) -> None:
"""
Restore the replay buffer contents from a csv file.
The csv file is assumed to include a list of transitions.
:param csv_dataset: A construct which holds the dataset parameters
"""
self.assert_not_frozen()
df = pd.read_csv(csv_dataset.filepath)
if len(df) > self.max_size[1]:
screen.warning(
"Warning! The number of transitions to load into the replay buffer ({}) is "
"bigger than the max size of the replay buffer ({}). The excessive transitions will "
"not be stored.".format(len(df), self.max_size[1]))
episode_ids = df['episode_id'].unique()
progress_bar = ProgressBar(len(episode_ids))
state_columns = [
col for col in df.columns if col.startswith('state_feature')
]
for e_id in episode_ids:
progress_bar.update(e_id)
df_episode_transitions = df[df['episode_id'] == e_id]
episode = Episode()
for (_, current_transition), (_, next_transition) in zip(
df_episode_transitions[:-1].iterrows(),
df_episode_transitions[1:].iterrows()):
state = np.array(
[current_transition[col] for col in state_columns])
next_state = np.array(
[next_transition[col] for col in state_columns])
episode.insert(
Transition(
state={'observation': state},
action=current_transition['action'],
reward=current_transition['reward'],
next_state={'observation': next_state},
game_over=False,
info={
'all_action_probabilities':
ast.literal_eval(
current_transition['all_action_probabilities'])
}))
# Set the last transition to end the episode
if csv_dataset.is_episodic:
episode.get_last_transition().game_over = True
self.store_episode(episode)
# close the progress bar
progress_bar.update(len(episode_ids))
progress_bar.close()
self.shuffle_episodes()
def load_csv(self, csv_dataset: CsvDataset,
input_filter: InputFilter) -> None:
"""
Restore the replay buffer contents from a csv file.
The csv file is assumed to include a list of transitions.
:param csv_dataset: A construct which holds the dataset parameters
:param input_filter: A filter used to filter the CSV data before feeding it to the memory.
"""
self.assert_not_frozen()
df = pd.read_csv(csv_dataset.filepath)
if len(df) > self.max_size[1]:
screen.warning(
"Warning! The number of transitions to load into the replay buffer ({}) is "
"bigger than the max size of the replay buffer ({}). The excessive transitions will "
"not be stored.".format(len(df), self.max_size[1]))
episode_ids = df["episode_id"].unique()
progress_bar = ProgressBar(len(episode_ids))
state_columns = [
col for col in df.columns if col.startswith("state_feature")
]
for e_id in episode_ids:
progress_bar.update(e_id)
df_episode_transitions = df[df["episode_id"] == e_id]
input_filter.reset()
if len(df_episode_transitions) < 2:
# we have to have at least 2 rows in each episode for creating a transition
continue
episode = Episode()
transitions = []
for (_, current_transition), (_, next_transition) in zip(
df_episode_transitions[:-1].iterrows(),
df_episode_transitions[1:].iterrows()):
state = np.array(
[current_transition[col] for col in state_columns])
next_state = np.array(
[next_transition[col] for col in state_columns])
transitions.append(
Transition(
state={"observation": state},
action=int(current_transition["action"]),
reward=current_transition["reward"],
next_state={"observation": next_state},
game_over=False,
info={
"all_action_probabilities":
ast.literal_eval(
current_transition["all_action_probabilities"])
},
), )
transitions = input_filter.filter(transitions, deep_copy=False)
for t in transitions:
episode.insert(t)
# Set the last transition to end the episode
if csv_dataset.is_episodic:
episode.get_last_transition().game_over = True
self.store_episode(episode)
# close the progress bar
progress_bar.update(len(episode_ids))
progress_bar.close()
