# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""
Implementation of learner class.
"""
import mindspore.nn as nn
[docs]class Learner(nn.Cell):
r"""
The base class of the learner.
Examples:
>>> from mindspore_rl.agent.learner import Learner
>>> from mindspore_rl.network import FullyConnectedNet
>>> class MyLearner(Learner):
... def init(self):
... super(MyLearner, self).init()
... self.target_network = FullyConnectedNet(4, 10, 2)
>>> my_learner = MyLearner()
>>> print(my_learner)
MyLearner<
(target_network): FullyConnectedNet<
(linear1): Dense<input_channels=4, output_channels=10, has_bias=True>
(linear2): Dense<input_channels=10, output_channels=2, has_bias=True>
(relu): ReLU<>
>
"""
def __init__(self):
super(Learner, self).__init__(auto_prefix=False)
[docs] def learn(self, samples):
"""
The interface for the learn function. The behavior of the `learn` function
depend on the user's implementation. Usually, it takes the `samples` form
replay buffer or other Tensors, and calculates the loss for updating the networks.
Args:
samples(Tensor): Sampling from the buffer.
Returns:
success, If the training success or not.
"""
raise NotImplementedError("Method should be overridden by subclass.")