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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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60
rl_coach/architectures/tensorflow_components/middlewares/fc_middleware.py
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@@ -27,42 +27,18 @@ class FCMiddlewareParameters(MiddlewareParameters):
def __init__(self, activation_function='relu',
scheme: Union[List, MiddlewareScheme] = MiddlewareScheme.Medium,
batchnorm: bool = False, dropout: bool = False,
name="middleware_fc_embedder"):
name="middleware_fc_embedder", dense_layer=Dense):
super().__init__(parameterized_class=FCMiddleware, activation_function=activation_function,
scheme=scheme, batchnorm=batchnorm, dropout=dropout, name=name)
scheme=scheme, batchnorm=batchnorm, dropout=dropout, name=name, dense_layer=dense_layer)
class FCMiddleware(Middleware):
schemes = {
MiddlewareScheme.Empty:
[],
# ppo
MiddlewareScheme.Shallow:
[
Dense([64])
],
# dqn
MiddlewareScheme.Medium:
[
Dense([512])
],
MiddlewareScheme.Deep: \
[
Dense([128]),
Dense([128]),
Dense([128])
]
}
def __init__(self, activation_function=tf.nn.relu,
scheme: MiddlewareScheme = MiddlewareScheme.Medium,
batchnorm: bool = False, dropout: bool = False,
name="middleware_fc_embedder"):
name="middleware_fc_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_FC_Embedding
self.layers = []
@@ -70,7 +46,7 @@ class FCMiddleware(Middleware):
self.layers.append(self.input)
if isinstance(self.scheme, MiddlewareScheme):
layers_params = FCMiddleware.schemes[self.scheme]
layers_params = self.schemes[self.scheme]
else:
layers_params = self.scheme
for idx, layer_params in enumerate(layers_params):
@@ -84,3 +60,29 @@ class FCMiddleware(Middleware):
self.output = self.layers[-1]
@property
def schemes(self):
return {
MiddlewareScheme.Empty:
[],
# ppo
MiddlewareScheme.Shallow:
[
self.dense_layer([64])
],
# dqn
MiddlewareScheme.Medium:
[
self.dense_layer([512])
],
MiddlewareScheme.Deep: \
[
self.dense_layer([128]),
self.dense_layer([128]),
self.dense_layer([128])
]
}