pre-release 0.10.0

rl_coach/architectures/tensorflow_components/middlewares/fc_middleware.py

@@ -0,0 +1,86 @@
+#
+# 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 Union, List
+
+import tensorflow as tf
+from rl_coach.architectures.tensorflow_components.middlewares.middleware import Middleware, MiddlewareParameters
+from rl_coach.base_parameters import MiddlewareScheme
+
+from rl_coach.architectures.tensorflow_components.architecture import batchnorm_activation_dropout, Dense
+from rl_coach.core_types import Middleware_FC_Embedding
+
+
+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"):
+        super().__init__(parameterized_class=FCMiddleware, activation_function=activation_function,
+                         scheme=scheme, batchnorm=batchnorm, dropout=dropout, name=name)
+
+
+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"):
+        super().__init__(activation_function=activation_function, batchnorm=batchnorm,
+                         dropout=dropout, scheme=scheme, name=name)
+        self.return_type = Middleware_FC_Embedding
+        self.layers = []
+
+    def _build_module(self):
+        self.layers.append(self.input)
+
+        if isinstance(self.scheme, MiddlewareScheme):
+            layers_params = FCMiddleware.schemes[self.scheme]
+        else:
+            layers_params = self.scheme
+        for idx, layer_params in enumerate(layers_params):
+            self.layers.append(
+                layer_params(self.layers[-1], name='{}_{}'.format(layer_params.__class__.__name__, idx))
+            )
+
+            self.layers.extend(batchnorm_activation_dropout(self.layers[-1], self.batchnorm,
+                                                            self.activation_function, self.dropout,
+                                                            self.dropout_rate, idx))
+
+        self.output = self.layers[-1]
+