Source code for rlgraph.components.distributions.normal
# Copyright 2018 The RLgraph authors. All Rights Reserved.
#
# 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
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# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from rlgraph import get_backend
from rlgraph.spaces import Tuple
from rlgraph.components.distributions.distribution import Distribution
if get_backend() == "tf":
import tensorflow as tf
elif get_backend() == "pytorch":
import torch
[docs]class Normal(Distribution):
"""
A Gaussian Normal distribution object defined by a tuple: mean, variance,
which is the same as "loc_and_scale".
"""
def __init__(self, scope="normal", **kwargs):
# Do not flatten incoming DataOps as we need more than one parameter in our parameterize graph_fn.
super(Normal, self).__init__(scope=scope, **kwargs)
def _graph_fn_get_distribution(self, parameters):
if get_backend() == "tf":
return tf.distributions.Normal(loc=parameters[0], scale=parameters[1])
elif get_backend() == "pytorch":
return torch.distributions.Normal(parameters[0], parameters[1])
def _graph_fn_sample_deterministic(self, distribution):
return distribution.mean()