def observation(self, indices=None, name=None):
with tf.variable_scope(name, default_name='MazeObservation'):
with tf.device('/cpu:0'):
obs = gym_tensorflow_module.environment_observation(self.instances, indices, T=tf.float32)
obs.set_shape((None,) + (11,))
#obs = tf.Print(obs, [obs], "obs=")
return tf.expand_dims(obs, axis=1)
def observation(self, indices=None, name=None):
if indices is None:
indices = np.arange(self.batch_size)
with tf.variable_scope(name, default_name='RivercrossingObservation'):
with tf.device('/cpu:0'):
obs = gym_tensorflow_module.environment_observation(self.instances, indices, T=tf.float32)
obs.set_shape((None,) + self.env_size + (self.num_stacked_frames,))
return obs
def render(self, indices=None):
if indices is None:
indices = np.arange(self.batch_size)
with tf.device('/cpu:0'):
obs = gym_tensorflow_module.environment_observation(self.instances,
indices,
T=tf.uint8)
return tf.gather(tf.constant(rgb_palette_uint8),
tf.cast(obs, tf.int32))
def observation(self, indices=None, name=None):
'''Returns current observation after preprocessing (skip, grayscale, warp, stack).\nMust be called ONCE each time step is called if num_stacked_frames > 1
'''
if indices is None:
batch_size = self.batch_size
indices = np.arange(self.batch_size)
else:
batch_size = indices.get_shape()[0]
with tf.variable_scope(name, default_name='AtariObservation'):
with tf.device('/cpu:0'):
obs = gym_tensorflow_module.environment_observation(self.instances, indices, T=tf.uint8)
obs = tf.gather(tf.constant(self.color_pallete), tf.cast(obs, tf.int32))
obs = tf.reduce_max(obs, axis=1)
obs = tf.image.resize_bilinear(obs, self.warp_size, align_corners=True)
obs.set_shape((batch_size,) + self.warp_size + (1,))
return obs