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parameter noise exploration - using Noisy Nets

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Gal Leibovich
2018-08-27 18:19:01 +03:00
parent 658b437079
commit 1aa2ab0590
49 changed files with 536 additions and 433 deletions
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63
rl_coach/architectures/tensorflow_components/middlewares/lstm_middleware.py
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@@ -18,7 +18,7 @@
import numpy as np
import tensorflow as tf
from rl_coach.architectures.tensorflow_components.architecture import batchnorm_activation_dropout
from rl_coach.architectures.tensorflow_components.architecture import batchnorm_activation_dropout, Dense
from rl_coach.architectures.tensorflow_components.middlewares.middleware import Middleware, MiddlewareParameters
from rl_coach.base_parameters import MiddlewareScheme
from rl_coach.core_types import Middleware_LSTM_Embedding
@@ -28,43 +28,19 @@ class LSTMMiddlewareParameters(MiddlewareParameters):
def __init__(self, activation_function='relu', number_of_lstm_cells=256,
scheme: MiddlewareScheme = MiddlewareScheme.Medium,
batchnorm: bool = False, dropout: bool = False,
name="middleware_lstm_embedder"):
name="middleware_lstm_embedder", dense_layer=Dense):
super().__init__(parameterized_class=LSTMMiddleware, activation_function=activation_function,
scheme=scheme, batchnorm=batchnorm, dropout=dropout, name=name)
scheme=scheme, batchnorm=batchnorm, dropout=dropout, name=name, dense_layer=dense_layer)
self.number_of_lstm_cells = number_of_lstm_cells
class LSTMMiddleware(Middleware):
schemes = {
MiddlewareScheme.Empty:
[],
# ppo
MiddlewareScheme.Shallow:
[
[64]
],
# dqn
MiddlewareScheme.Medium:
[
[512]
],
MiddlewareScheme.Deep: \
[
[128],
[128],
[128]
]
}
def __init__(self, activation_function=tf.nn.relu, number_of_lstm_cells: int=256,
scheme: MiddlewareScheme = MiddlewareScheme.Medium,
batchnorm: bool = False, dropout: bool = False,
name="middleware_lstm_embedder"):
name="middleware_lstm_embedder", dense_layer=Dense):
super().__init__(activation_function=activation_function, batchnorm=batchnorm,
dropout=dropout, scheme=scheme, name=name)
dropout=dropout, scheme=scheme, name=name, dense_layer=dense_layer)
self.return_type = Middleware_LSTM_Embedding
self.number_of_lstm_cells = number_of_lstm_cells
self.layers = []
@@ -83,7 +59,7 @@ class LSTMMiddleware(Middleware):
# optionally insert some dense layers before the LSTM
if isinstance(self.scheme, MiddlewareScheme):
layers_params = LSTMMiddleware.schemes[self.scheme]
layers_params = self.schemes[self.scheme]
else:
layers_params = self.scheme
for idx, layer_params in enumerate(layers_params):
@@ -111,3 +87,30 @@ class LSTMMiddleware(Middleware):
lstm_c, lstm_h = lstm_state
self.state_out = (lstm_c[:1, :], lstm_h[:1, :])
self.output = tf.reshape(lstm_outputs, [-1, self.number_of_lstm_cells])
@property
def schemes(self):
return {
MiddlewareScheme.Empty:
[],
# ppo
MiddlewareScheme.Shallow:
[
[64]
],
# dqn
MiddlewareScheme.Medium:
[
[512]
],
MiddlewareScheme.Deep: \
[
[128],
[128],
[128]
]
}