pre-release 0.10.0

rl_coach/architectures/tensorflow_components/heads/naf_head.py

@@ -0,0 +1,88 @@
+#
+# 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.
+#
+
+import tensorflow as tf
+from rl_coach.base_parameters import AgentParameters
+from rl_coach.spaces import BoxActionSpace
+from rl_coach.spaces import SpacesDefinition
+
+from rl_coach.architectures.tensorflow_components.heads.head import Head, HeadParameters
+from rl_coach.core_types import QActionStateValue
+
+
+class NAFHeadParameters(HeadParameters):
+    def __init__(self, activation_function: str ='tanh', name: str='naf_head_params'):
+        super().__init__(parameterized_class=NAFHead, activation_function=activation_function, name=name)
+
+
+class NAFHead(Head):
+    def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str,
+                 head_idx: int = 0, loss_weight: float = 1., is_local: bool = True,activation_function: str='relu'):
+        super().__init__(agent_parameters, spaces, network_name, head_idx, loss_weight, is_local, activation_function)
+        if not isinstance(self.spaces.action, BoxActionSpace):
+            raise ValueError("NAF works only for continuous action spaces (BoxActionSpace)")
+
+        self.name = 'naf_q_values_head'
+        self.num_actions = self.spaces.action.shape[0]
+        self.output_scale = self.spaces.action.max_abs_range
+        self.return_type = QActionStateValue
+        if agent_parameters.network_wrappers[self.network_name].replace_mse_with_huber_loss:
+            self.loss_type = tf.losses.huber_loss
+        else:
+            self.loss_type = tf.losses.mean_squared_error
+
+    def _build_module(self, input_layer):
+        # NAF
+        self.action = tf.placeholder(tf.float32, [None, self.num_actions], name="action")
+        self.input = self.action
+
+        # V Head
+        self.V = tf.layers.dense(input_layer, 1, name='V')
+
+        # mu Head
+        mu_unscaled = tf.layers.dense(input_layer, self.num_actions, activation=self.activation_function, name='mu_unscaled')
+        self.mu = tf.multiply(mu_unscaled, self.output_scale, name='mu')
+
+        # A Head
+        # l_vector is a vector that includes a lower-triangular matrix values
+        self.l_vector = tf.layers.dense(input_layer, (self.num_actions * (self.num_actions + 1)) / 2, name='l_vector')
+
+        # Convert l to a lower triangular matrix and exponentiate its diagonal
+
+        i = 0
+        columns = []
+        for col in range(self.num_actions):
+            start_row = col
+            num_non_zero_elements = self.num_actions - start_row
+            zeros_column_part = tf.zeros_like(self.l_vector[:, 0:start_row])
+            diag_element = tf.expand_dims(tf.exp(self.l_vector[:, i]), 1)
+            non_zeros_non_diag_column_part = self.l_vector[:, (i + 1):(i + num_non_zero_elements)]
+            columns.append(tf.concat([zeros_column_part, diag_element, non_zeros_non_diag_column_part], axis=1))
+            i += num_non_zero_elements
+        self.L = tf.transpose(tf.stack(columns, axis=1), (0, 2, 1))
+
+        # P = L*L^T
+        self.P = tf.matmul(self.L, tf.transpose(self.L, (0, 2, 1)))
+
+        # A = -1/2 * (u - mu)^T * P * (u - mu)
+        action_diff = tf.expand_dims(self.action - self.mu, -1)
+        a_matrix_form = -0.5 * tf.matmul(tf.transpose(action_diff, (0, 2, 1)), tf.matmul(self.P, action_diff))
+        self.A = tf.reshape(a_matrix_form, [-1, 1])
+
+        # Q Head
+        self.Q = tf.add(self.V, self.A, name='Q')
+
+        self.output = self.Q