Source code for openrl.utils.callbacks.processbar_callback
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2023 The OpenRL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""""""
import warnings
try:
from tqdm import TqdmExperimentalWarning
# Remove experimental warning
warnings.filterwarnings("ignore", category=TqdmExperimentalWarning)
from tqdm.rich import tqdm
except ImportError:
# Rich not installed, we only throw an error
# if the progress bar is used
tqdm = None
from openrl.utils.callbacks.callbacks import BaseCallback
[docs]class ProgressBarCallback(BaseCallback):
"""
Display a progress bar when training SB3 agent
using tqdm and rich packages.
"""
def __init__(self) -> None:
super().__init__()
if tqdm is None:
raise ImportError(
"You must install tqdm and rich in order to use the progress bar"
" callback. "
)
self.pbar = None
def _on_training_start(self) -> None:
# Initialize progress bar
# Remove time_steps that were done in previous training sessions
self.pbar = tqdm(
total=self.locals["total_time_steps"] - self.agent.num_time_steps
)
def _on_step(self) -> bool:
# Update progress bar, we do num_envs steps per call to `env.step()`
self.pbar.update(self.training_env.parallel_env_num)
return True
def _on_training_end(self) -> None:
# Flush and close progress bar
self.pbar.refresh()
self.pbar.close()