# Copyright 2020,2021 Sony Corporation.
# Copyright 2021,2022,2023 Sony Group Corporation.
#
# 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
#
# http://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 numpy as np
from nnabla_rl.hook import Hook
from nnabla_rl.logger import logger
from nnabla_rl.utils.evaluator import EpisodicEvaluator
[docs]class EvaluationHook(Hook):
"""Hook to run evaluation during training.
Args:
env (gym.Env): Environment to run the evaluation
evaluator (Callable[[nnabla_rl.algorithm.Algorithm, gym.Env], List[float]]):
Evaluator which runs the actual evaluation.
Defaults to :py:class:`EpisodicEvaluator <nnabla_rl.utils.evaluator.EpisodicEvaluator>`.
timing (int): Evaluation interval. Defaults to 1000 iteration.
writer (nnabla_rl.writer.Writer, optional): Writer instance to save/print the evaluation results.
Defaults to None.
"""
def __init__(self, env, evaluator=EpisodicEvaluator(), timing=1000, writer=None):
super(EvaluationHook, self).__init__(timing=timing)
self._env = env
self._evaluator = evaluator
self._timing = timing
self._writer = writer
[docs] def on_hook_called(self, algorithm):
iteration_num = algorithm.iteration_num
logger.info(
'Starting evaluation at iteration {}.'.format(iteration_num))
returns = self._evaluator(algorithm, self._env)
mean = np.mean(returns)
std_dev = np.std(returns)
median = np.median(returns)
logger.info('Evaluation results at iteration {}. mean: {} +/- std: {}, median: {}'.format(
iteration_num, mean, std_dev, median))
if self._writer is not None:
minimum = np.min(returns)
maximum = np.max(returns)
# From python 3.6 or above, the dictionary preserves insertion order
scalar_results = {}
scalar_results['mean'] = mean
scalar_results['std_dev'] = std_dev
scalar_results['min'] = minimum
scalar_results['max'] = maximum
scalar_results['median'] = median
self._writer.write_scalar(algorithm.iteration_num, scalar_results)
histogram_results = {}
histogram_results['returns'] = returns
self._writer.write_histogram(algorithm.iteration_num, histogram_results)