pre-release 0.10.0

rl_coach/architectures/architecture.py

@@ -0,0 +1,71 @@
+#
+# 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 rl_coach.base_parameters import AgentParameters
+from rl_coach.spaces import SpacesDefinition
+
+
+class Architecture(object):
+    def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, name: str= ""):
+        """
+        :param agent_parameters: the agent parameters
+        :param spaces: the spaces (observation, action, etc.) definition of the agent
+        :param name: the name of the network
+        """
+        # spaces
+        self.spaces = spaces
+
+        self.name = name
+        self.network_wrapper_name = self.name.split('/')[0]  # the name can be main/online and the network_wrapper_name will be main
+        self.full_name = "{}/{}".format(agent_parameters.full_name_id, name)
+        self.network_parameters = agent_parameters.network_wrappers[self.network_wrapper_name]
+        self.batch_size = self.network_parameters.batch_size
+        self.learning_rate = self.network_parameters.learning_rate
+        self.optimizer = None
+        self.ap = agent_parameters
+
+    def get_model(self):
+        pass
+
+    def predict(self, inputs):
+        pass
+
+    def train_on_batch(self, inputs, targets):
+        pass
+
+    def get_weights(self):
+        pass
+
+    def set_weights(self, weights, rate=1.0):
+        pass
+
+    def reset_accumulated_gradients(self):
+        pass
+
+    def accumulate_gradients(self, inputs, targets):
+        pass
+
+    def apply_and_reset_gradients(self, gradients):
+        pass
+
+    def apply_gradients(self, gradients):
+        pass
+
+    def get_variable_value(self, variable):
+        pass
+
+    def set_variable_value(self, assign_op, value, placeholder=None):
+        pass