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Move embedder, middleware, and head parameters to framework agnostic modules. (#45)

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Part of #28
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Sina Afrooze
2018-10-29 14:46:40 -07:00
committed by Scott Leishman
parent 16b3e99f37
commit a888226641
60 changed files with 410 additions and 330 deletions
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rl_coach/architectures/head_parameters.py Normal file
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#
# Copyright (c) 2017 Intel 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.
#
from typing import Type
from rl_coach.base_parameters import NetworkComponentParameters
class HeadParameters(NetworkComponentParameters):
def __init__(self, parameterized_class_name: str, activation_function: str = 'relu', name: str= 'head',
num_output_head_copies: int=1, rescale_gradient_from_head_by_factor: float=1.0,
loss_weight: float=1.0, dense_layer=None):
super().__init__(dense_layer=dense_layer)
self.activation_function = activation_function
self.name = name
self.num_output_head_copies = num_output_head_copies
self.rescale_gradient_from_head_by_factor = rescale_gradient_from_head_by_factor
self.loss_weight = loss_weight
self.parameterized_class_name = parameterized_class_name
class PPOHeadParameters(HeadParameters):
def __init__(self, activation_function: str ='tanh', name: str='ppo_head_params',
num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
loss_weight: float = 1.0, dense_layer=None):
super().__init__(parameterized_class_name="PPOHead", activation_function=activation_function, name=name,
dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
loss_weight=loss_weight)
class VHeadParameters(HeadParameters):
def __init__(self, activation_function: str ='relu', name: str='v_head_params',
num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
loss_weight: float = 1.0, dense_layer=None):
super().__init__(parameterized_class_name="VHead", activation_function=activation_function, name=name,
dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
loss_weight=loss_weight)
class CategoricalQHeadParameters(HeadParameters):
def __init__(self, activation_function: str ='relu', name: str='categorical_q_head_params',
num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
loss_weight: float = 1.0, dense_layer=None):
super().__init__(parameterized_class_name="CategoricalQHead", activation_function=activation_function, name=name,
dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
loss_weight=loss_weight)
class RegressionHeadParameters(HeadParameters):
def __init__(self, activation_function: str ='relu', name: str='q_head_params',
num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
loss_weight: float = 1.0, dense_layer=None, scheme=None):
super().__init__(parameterized_class_name="RegressionHead", activation_function=activation_function, name=name,
dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
loss_weight=loss_weight)
class DDPGActorHeadParameters(HeadParameters):
def __init__(self, activation_function: str ='tanh', name: str='policy_head_params', batchnorm: bool=True,
num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
loss_weight: float = 1.0, dense_layer=None):
super().__init__(parameterized_class_name="DDPGActor", activation_function=activation_function, name=name,
dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
loss_weight=loss_weight)
self.batchnorm = batchnorm
class DNDQHeadParameters(HeadParameters):
def __init__(self, activation_function: str ='relu', name: str='dnd_q_head_params',
num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
loss_weight: float = 1.0, dense_layer=None):
super().__init__(parameterized_class_name="DNDQHead", activation_function=activation_function, name=name,
dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
loss_weight=loss_weight)
class DuelingQHeadParameters(HeadParameters):
def __init__(self, activation_function: str ='relu', name: str='dueling_q_head_params',
num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
loss_weight: float = 1.0, dense_layer=None):
super().__init__(parameterized_class_name="DuelingQHead", activation_function=activation_function, name=name,
dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
loss_weight=loss_weight)
class MeasurementsPredictionHeadParameters(HeadParameters):
def __init__(self, activation_function: str ='relu', name: str='measurements_prediction_head_params',
num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
loss_weight: float = 1.0, dense_layer=None):
super().__init__(parameterized_class_name="MeasurementsPredictionHead", activation_function=activation_function, name=name,
dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
loss_weight=loss_weight)
class NAFHeadParameters(HeadParameters):
def __init__(self, activation_function: str ='tanh', name: str='naf_head_params',
num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
loss_weight: float = 1.0, dense_layer=None):
super().__init__(parameterized_class_name="NAFHead", activation_function=activation_function, name=name,
dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
loss_weight=loss_weight)
class PolicyHeadParameters(HeadParameters):
def __init__(self, activation_function: str ='tanh', name: str='policy_head_params',
num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
loss_weight: float = 1.0, dense_layer=None):
super().__init__(parameterized_class_name="PolicyHead", activation_function=activation_function, name=name,
dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
loss_weight=loss_weight)
class PPOVHeadParameters(HeadParameters):
def __init__(self, activation_function: str ='relu', name: str='ppo_v_head_params',
num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
loss_weight: float = 1.0, dense_layer=None):
super().__init__(parameterized_class_name="PPOVHead", activation_function=activation_function, name=name,
dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
loss_weight=loss_weight)
class QHeadParameters(HeadParameters):
def __init__(self, activation_function: str ='relu', name: str='q_head_params',
num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
loss_weight: float = 1.0, dense_layer=None):
super().__init__(parameterized_class_name="QHead", activation_function=activation_function, name=name,
dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
loss_weight=loss_weight)
class QuantileRegressionQHeadParameters(HeadParameters):
def __init__(self, activation_function: str ='relu', name: str='quantile_regression_q_head_params',
num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
loss_weight: float = 1.0, dense_layer=None):
super().__init__(parameterized_class_name="QuantileRegressionQHead", activation_function=activation_function, name=name,
dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
loss_weight=loss_weight)
class RainbowQHeadParameters(HeadParameters):
def __init__(self, activation_function: str ='relu', name: str='rainbow_q_head_params',
num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0,
loss_weight: float = 1.0, dense_layer=None):
super().__init__(parameterized_class_name="RainbowQHead", activation_function=activation_function, name=name,
dense_layer=dense_layer, num_output_head_copies=num_output_head_copies,
rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor,
loss_weight=loss_weight)