BCQ variant on top of DDQN (#276)

* kNN based model for predicting which actions to drop
* fix for seeds with batch rl

rl_coach/architectures/tensorflow_components/heads/classification_head.py

@@ -0,0 +1,60 @@
+#
+# 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
+from rl_coach.base_parameters import AgentParameters
+from rl_coach.spaces import SpacesDefinition, BoxActionSpace, DiscreteActionSpace
+
+
+class ClassificationHead(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):
+        super().__init__(agent_parameters, spaces, network_name, head_idx, loss_weight, is_local, activation_function,
+                         dense_layer=dense_layer)
+        self.name = 'classification_head'
+        if isinstance(self.spaces.action, BoxActionSpace):
+            self.num_actions = 1
+        elif isinstance(self.spaces.action, DiscreteActionSpace):
+            self.num_actions = len(self.spaces.action.actions)
+        else:
+            raise ValueError(
+                'ClassificationHead does not support action spaces of type: {class_name}'.format(
+                    class_name=self.spaces.action.__class__.__name__,
+                )
+            )
+
+    def _build_module(self, input_layer):
+        # Standard classification Network
+        self.class_values = self.output = self.dense_layer(self.num_actions)(input_layer, name='output')
+
+        self.output = tf.nn.softmax(self.class_values)
+
+        # calculate cross entropy loss
+        self.target = tf.placeholder(tf.float32, shape=(None, self.num_actions), name="target")
+        self.loss = tf.nn.softmax_cross_entropy_with_logits(labels=self.target, logits=self.class_values)
+        tf.losses.add_loss(self.loss)
+
+    def __str__(self):
+        result = [
+            "Dense (num outputs = {})".format(self.num_actions)
+        ]
+        return '\n'.join(result)
+
+