TD3 (#338)

rl_coach/architectures/tensorflow_components/heads/__init__.py

@@ -16,6 +16,7 @@ from .sac_head import SACPolicyHead
 from .sac_q_head import SACQHead
 from .classification_head import ClassificationHead
 from .cil_head import RegressionHead
+from .td3_v_head import TD3VHead
 from .ddpg_v_head import DDPGVHead
 
 __all__ = [
@@ -37,5 +38,6 @@ __all__ = [
     'SACQHead',
     'ClassificationHead',
     'RegressionHead',
+    'TD3VHead'
     'DDPGVHead'
 ]

rl_coach/architectures/tensorflow_components/heads/head.py

@@ -13,7 +13,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
-from typing import Type
 
 import numpy as np
 import tensorflow as tf
@@ -22,7 +21,7 @@ from rl_coach.architectures.tensorflow_components.layers import Dense, convert_l
 from rl_coach.base_parameters import AgentParameters
 from rl_coach.spaces import SpacesDefinition
 from rl_coach.utils import force_list
-
+from rl_coach.architectures.tensorflow_components.utils import squeeze_tensor
 
 # Used to initialize weights for policy and value output layers
 def normalized_columns_initializer(std=1.0):
@@ -72,8 +71,9 @@ class Head(object):
         :param input_layer: the input to the graph
         :return: the output of the last layer and the target placeholder
         """
+
         with tf.variable_scope(self.get_name(), initializer=tf.contrib.layers.xavier_initializer()):
-            self._build_module(input_layer)
+            self._build_module(squeeze_tensor(input_layer))
 
             self.output = force_list(self.output)
             self.target = force_list(self.target)

rl_coach/architectures/tensorflow_components/heads/td3_v_head.py

@@ -0,0 +1,67 @@
+#
+# Copyright (c) 2019 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.architectures.tensorflow_components.layers import Dense
+from rl_coach.architectures.tensorflow_components.heads.head import Head, normalized_columns_initializer
+from rl_coach.base_parameters import AgentParameters
+from rl_coach.core_types import VStateValue
+from rl_coach.spaces import SpacesDefinition
+
+
+class TD3VHead(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',
+                 dense_layer=Dense, initializer='xavier'):
+        super().__init__(agent_parameters, spaces, network_name, head_idx, loss_weight, is_local, activation_function,
+                         dense_layer=dense_layer)
+        self.name = 'td3_v_values_head'
+        self.return_type = VStateValue
+        self.loss_type = []
+        self.initializer = initializer
+        self.loss = []
+        self.output = []
+
+    def _build_module(self, input_layer):
+        # Standard V Network
+        q_outputs = []
+        self.target = tf.placeholder(tf.float32, shape=(None, 1), name="q_networks_min_placeholder")
+
+        for i in range(input_layer.shape[0]): # assuming that the actual size is 2, as there are two critic networks
+            if self.initializer == 'normalized_columns':
+                q_outputs.append(self.dense_layer(1)(input_layer[i], name='q_output_{}'.format(i + 1),
+                                                     kernel_initializer=normalized_columns_initializer(1.0)))
+            elif self.initializer == 'xavier' or self.initializer is None:
+                q_outputs.append(self.dense_layer(1)(input_layer[i], name='q_output_{}'.format(i + 1)))
+
+            self.output.append(q_outputs[i])
+            self.loss.append(tf.reduce_mean((self.target-q_outputs[i])**2))
+
+        self.output.append(tf.reduce_min(q_outputs, axis=0))
+        self.output.append(tf.reduce_mean(self.output[0]))
+        self.loss = sum(self.loss)
+        tf.losses.add_loss(self.loss)
+
+    def __str__(self):
+        result = [
+            "Q1 Action-Value Stream",
+            "\tDense (num outputs = 1)",
+            "Q2 Action-Value Stream",
+            "\tDense (num outputs = 1)",
+            "Min (Q1, Q2)"
+        ]
+        return '\n'.join(result)