def __init__(self, interface):
super().__init__(interface)
self.bootstrap_state_input = tf.placeholder(
tf.float32, [*self.interface.state_shape])
self.features = parts.conv_body(self.state_input)
self.nonspacial_probs, self.spacial_probs_x, self.spacial_probs_y = self._probs_from_features(
self.features)
def features(self):
conv_features = parts.conv_body(self.state_input)
unit_features = tf.reduce_sum(self.self_attention(
self.unit_embeddings_input, bias=0),
axis=1)
logits = tf.concat(
[conv_features, unit_features, self.other_features_input], axis=1)
logits = tf.layers.dense(logits,
units=200,
activation=tf.nn.leaky_relu,
name='feature_layer')
logits = tf.layers.dense(logits,
units=200,
activation=tf.nn.tanh,
name='feature_layer2')
return logits
def features(self):
conv_features = parts.conv_body(self.state_input)
unit_features = tf.reduce_sum(self.self_attention(
self.unit_embeddings_input, bias=0),
axis=1)
return tf.concat([conv_features, unit_features], axis=1)
def bootstrap_value(self):
return tf.squeeze(parts.value_head(
parts.conv_body(tf.expand_dims(self.bootstrap_state_input,
axis=0))),
axis=0)