DDPG Critic Head Bug Fix (#344)

* A bug fix for DDPG, where the update to the policy network was based on the sum of the critic's Q predictions on the batch instead of their mean

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 .ddpg_v_head import DDPGVHead
 
 __all__ = [
     'CategoricalQHead',
@@ -35,5 +36,6 @@ __all__ = [
     'SACPolicyHead',
     'SACQHead',
     'ClassificationHead',
-    'RegressionHead'
+    'RegressionHead',
+    'DDPGVHead'
 ]

rl_coach/architectures/tensorflow_components/heads/ddpg_v_head.py

@@ -0,0 +1,39 @@
+#
+# 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.architectures.tensorflow_components.heads import VHead
+from rl_coach.architectures.tensorflow_components.layers import Dense
+from rl_coach.base_parameters import AgentParameters
+from rl_coach.spaces import SpacesDefinition
+
+
+class DDPGVHead(VHead):
+    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='normalized_columns'):
+        super().__init__(agent_parameters, spaces, network_name, head_idx, loss_weight, is_local, activation_function,
+                         dense_layer=dense_layer, initializer=initializer)
+
+    def _build_module(self, input_layer):
+        super()._build_module(input_layer)
+        self.output = [self.output, tf.reduce_mean(self.output)]
+
+    def __str__(self):
+        result = [
+            "Dense (num outputs = 1)"
+        ]
+        return '\n'.join(result)