Source code for mindspore_rl.utils.noise

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"""
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


[docs]class OUNoise(nn.Cell): r""" Perform Ornstein-Uhlenbeck (OU) noise base on actions. Set zero-mean normal distribution as :math:`N(0, stddev)`, Then the next temporal value is :math:`x\_next = (1 - damping) * x - N(0, stddev)`, The action with OU Noise is :math:`action += x\_next`. 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