def _after_reset(self, observation):
if not self.enabled: return
# Reset the stat count
self.stats_recorder.after_reset(observation)
# Close any existing video recorder
if self.video_recorder:
self._close_video_recorder()
# Start recording the next video.
#
# TODO: calculate a more correct 'episode_id' upon merge
self.video_recorder = video_recorder.VideoRecorder(
env=self.env,
base_path=os.path.join(
self.directory,
'{}.video.{}.video{:06}'.format(self.file_prefix,
self.file_infix,
self.episode_id)),
metadata={'episode_id': self.episode_id},
enabled=self._video_enabled(),
)
self.video_recorder.capture_frame()
# Bump *after* all reset activity has finished
self.episode_id += 1
self.flush()
def _reset_video_recorder(self):
# Close any existing video recorder
if self.video_recorder:
self._close_video_recorder()
# Start recording the next video.
#
# TODO: calculate a more correct 'episode_id' upon merge
self.video_recorder = video_recorder.VideoRecorder(
env=self.env,
base_path=os.path.join(self.directory, '{}.video.{}.video{:06}'.format(self.file_prefix, self.file_infix, self.episode_id)),
metadata={'episode_id': self.episode_id},
enabled=self._video_enabled(),
)
self.video_recorder.capture_frame()
def main(_):
env = create_env(FLAGS.env_id, seed=FLAGS.seed)
if FLAGS.movie_path:
video = video_recorder.VideoRecorder(env=env,
base_path=FLAGS.movie_path)
else:
video = None
tf.set_random_seed(FLAGS.seed)
with tf.Session() as sess:
agent = Agent(env, sess)
agent.run(FLAGS.num_episodes, video)
if video is not None:
print("Saving movie to {}.mp4".format(FLAGS.movie_path))
video.close()
clearn.get_reward(img_test)
saver = tf.train.Saver()
saver.restore(sess, "train_weights/weights")
sess.run(tf.global_variables_initializer())
r, enc_img = sess.run(
[clearn.reward, clearn.enc_img], {
graph_input: dummy_array,
graph_input2: dummy_array,
img_test: state
})
reward = r
# print("reward =", r)
return reward, enc_img
recorder = video_recorder.VideoRecorder(env, "numpy_vids/vid81.mp4")
time_steps_lived = []
clearn = CartPoleLearn()
c = 0
expert_vid = np.load("numpy_vids/vid%d.npy" % 81)
expert_average = np.zeros(shape=state_size)
sift = cv2.xfeatures2d.SIFT_create(nfeatures=6)
for e in range(50000):
env.reset()
recorder.env = env
recorded_frames = None
ts = 1
cp_state = None
graph_input = tf.placeholder(tf.float32, (batch_size, 36, 64, 3,)) # expert vid
graph_input2 = tf.placeholder(tf.float32, (batch_size, 36, 64, 3,)) # recorded vid
img_test = tf.placeholder(tf.float32, (1, 36, 64, 3,))
clearn.build_encoder(graph_input, graph_input2)
clearn.get_reward(img_test)
saver = tf.train.Saver()
saver.restore(sess, "train_weights/weights")
sess.run(tf.global_variables_initializer())
r, enc_img = sess.run([clearn.reward, clearn.enc_img],
{graph_input:dummy_array , graph_input2: dummy_array, img_test: state})
reward = r
# print("reward =", r)
return reward, enc_img
recorder = video_recorder.VideoRecorder(env, "/home/bayes/Academic/DeepRL/Project/IMLearnPG/numpy_vids/vid81.mp4")
time_steps_lived = []
#clearn = CartPoleLearn()
c=0
expert_vid = np.load("numpy_vids/vid%d.npy" % 81)
pca = PCA(n_components=4)
expert_vid = list(map(lambda x: normalize_img(crop_image_gray(x).ravel()), expert_vid))
expert_vid = pca.fit_transform(expert_vid)
expert_average = np.zeros(shape=state_size)
exp_e_frame = np.zeros(shape=state_size)
