text: # Copyright 2022 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.
  # ============================================================================
  """
  Noise class for exploration.
  """
  
  import mindspore.nn as nn
  from mindspore.ops import operations as P
  from mindspore.common.parameter import Parameter
  import mindspore.nn.probability.distribution as msd
  from mindspore.common.initializer import initializer
  
  
  class OUNoise(nn.Cell):
      r"""
      Perform Ornstein-Uhlenbeck (OU) noise base on actions.
  
      Args:
          stddev (float): stddev of Ornstein-Uhlenbeck (OU) noise.
          damping (float): damping of Ornstein-Uhlenbeck (OU) noise.
          action_shape(tuple): action shape.
  
      Inputs:
          - **actions** (Tensor) - Actions before perferming noise.
  
      Outputs:
          - **actions** (Tensor) - Actions after perferming noise.
  
      Examples:
          >>> import numpy as np
          >>> from mindspore import Tensor
          >>> from mindspore_rl.utils import OUNoise
          >>> action_shape = (6,)
          >>> actions = Tensor(np.ones(action_shape))
          >>> net = OUNoise(stddev=0.2, damping=0.15, action_shape=action_shape)
          >>> actions = net(actions)
          >>> print(actions.shape)
          (6,)
      """
      def __init__(self, stddev, damping, action_shape):
          super(OUNoise, self).__init__()
          self.damping = damping
          self.x = Parameter(initializer(0, action_shape), name="x", requires_grad=False)
          self.normal = msd.Normal(0., stddev)
          self.assign = P.Assign()
  
      def construct(self, actions):
          noise = self.normal.sample(actions.shape)
          self.x = (1.0 - self.damping) * self.x + noise
          return actions + self.x
  