def build_perception(self, obs):
logger.info("Building %s perception..." % self.scope)
with tf.variable_scope(self.scope):
m_conv = blocks.cnn(obs["minimap"], "minimap_feat")
s_conv = blocks.cnn(obs["screen"], "screen_feat")
self.s = blocks.concat(m_conv, s_conv, obs["non_spatial"], "percept_concat")
if self.use_batch_norm:
self.s = tf.contrib.layers.batch_norm(self.s)
self.s_fc = blocks.fully_connected(self.s, self.s_dim, "%s_perception" % self.scope)
if self.use_batch_norm:
self.s_fc = tf.contrib.layers.batch_norm(self.s_fc)
def _build_network(self):
m_conv = blocks.cnn(self.obs["minimap"], "minimap_feat")
s_conv = blocks.cnn(self.obs["screen"], "screen_feat")
state_representation = blocks.concat(m_conv, s_conv,
self.obs["non_spatial"],
"state_rep")
fc = blocks.fully_connected(state_representation, 256, "fc")
self.pi = {
"spatial":
blocks.spatial_action(state_representation, "spatial_act_pi"),
"non_spatial":
blocks.non_spatial_action(fc, "non_spatial_act_pi")
}
self.vf = blocks.build_value(fc, "value")
def _build_network(self):
m_conv = blocks.cnn(self.obs["minimap"],
"minimap_feat",
kernel_size=[8, 4],
stride=[4, 2])
s_conv = blocks.cnn(self.obs["screen"],
"screen_feat",
kernel_size=[8, 4],
stride=[4, 2])
non_spatial = blocks.non_spatial_feat_atari(self.obs["non_spatial"],
"ns_feat")
state_representation = tf.concat(
[layers.flatten(m_conv),
layers.flatten(s_conv), non_spatial],
axis=1,
name="state_rep")
fc = blocks.fully_connected(state_representation, 256, "fc")
spatial_action_x = blocks.spatial_action_atari(fc,
self.s_size,
"spatial_act_x",
transpose=True)
spatial_action_y = blocks.spatial_action_atari(fc, self.s_size,
"spatial_act_y")
spatial_action = layers.flatten(tf.multiply(spatial_action_x,
spatial_action_y),
scope="spatial_act_pi")
self.pi = {
"spatial": spatial_action,
"non_spatial": blocks.non_spatial_action(fc, "non_spatial_act_pi")
}
self.vf = blocks.build_value(fc, "value")