def rollout(self,
env,
agent,
directory,
record_video=False,
timeout=8,
num_episode=10,
record_trajectory=False):
"""Rollout and save.
Args:
env: the RL environment
agent: the RL agent
directory: directory where the output of the rollout is saved
record_video: record the video
timeout: timeout step if the agent is stuck
num_episode: number of rollout episode
record_trajectory: record the ground truth trajectory
Returns:
percentage of success during this rollout
"""
print('#######################################')
print('Rolling out...')
print('#######################################')
all_frames = []
ep_observation, ep_action, ep_agn = [], [], []
black_frame = pad_image(env.render(mode='rgb_array')) * 0.0
goal_sampled = 0
timeout_count, success = 0, 0
for ep in range(num_episode):
s = self.reset(env, agent, self.cfg.diverse_scene_content)
all_frames += [black_frame] * 10
g_text, p = env.sample_goal()
if env.all_goals_satisfied:
s = self.reset(env, agent, True)
g, p = env.sample_goal()
goal_sampled += 1
g = np.squeeze(self.encode_fn(g_text))
current_goal_repetition = 0
for t in range(self.cfg.max_episode_length):
prob = self.epsilon if record_trajectory else 0.0
action = agent.step(s, g, env, explore_prob=prob)
s_tp1, r, _, _ = env.step(
action,
record_achieved_goal=True,
goal=p,
atomic_goal=self.cfg.record_atomic_instruction)
ag = env.get_achieved_goals()
s = s_tp1
all_frames.append(
add_text(pad_image(env.render(mode='rgb_array')), g_text))
current_goal_repetition += 1
if record_trajectory:
ep_observation.append(env.get_direct_obs().tolist())
ep_action.append(action)
ep_agn.append(len(ag))
sample_new_goal = False
if r > env.shape_val:
for _ in range(5):
all_frames.append(
add_text(
pad_image(env.render(mode='rgb_array')),
g_text,
color='green'))
success += 1
sample_new_goal = True
if current_goal_repetition >= timeout:
all_frames.append(
add_text(pad_image(env.render(mode='rgb_array')), 'time out :('))
timeout_count += 1
sample_new_goal = True
if sample_new_goal:
g, p = env.sample_goal()
if env.all_goals_satisfied:
break
g_text = g
g = np.squeeze(self.encode_fn(g))
current_goal_repetition = 0
goal_sampled += 1
print('Rollout finished')
print('{} instrutctions tried given'.format(goal_sampled))
print('{} instructions timed out'.format(timeout_count))
if record_video:
save_video(np.uint8(all_frames), directory, fps=5)
print('Video saved...')
if record_trajectory:
print('Recording trajectory...')
datum = {
'obs': ep_observation,
'action': ep_action,
'achieved goal': ep_agn,
}
save_json(datum, directory[:-4] + '_trajectory.json')
return success / float(num_episode)
def rollout(self,
env,
agent,
directory,
record_video=False,
timeout=8,
num_episode=10,
record_trajectory=False):
"""Rollout and save.
Args:
env: the RL environment
agent: the RL agent
directory: directory where the output of the rollout is saved
record_video: record the video
timeout: timeout step if the agent is stuck
num_episode: number of rollout episode
record_trajectory: record the ground truth trajectory
Returns:
percentage of success during this rollout
"""
print('\n#######################################')
print('Rolling out...')
print('#######################################')
# randomly change subset of embedding
if self._use_synonym_for_rollout and self.cfg.embedding_type == 'random':
original_embedding = agent.randomize_partial_word_embedding(10)
all_frames = []
ep_observation, ep_action, ep_agn = [], [], []
black_frame = pad_image(env.render(mode='rgb_array')) * 0.0
goal_sampled = 0
timeout_count, success = 0, 0
for ep in range(num_episode):
s = env.reset(self.cfg.diverse_scene_content)
all_frames += [black_frame] * 10
g_text, p = env.sample_goal()
if env.all_goals_satisfied:
s = env.reset(True)
g, p = env.sample_goal()
goal_sampled += 1
g = self.encode_fn(g_text)
g = np.squeeze(pad_to_max_length([g], self.cfg.max_sequence_length)[0])
if self._use_synonym_for_rollout and self.cfg.embedding_type != 'random':
# use unseen lexicons for test
g = paraphrase_sentence(
self.decode_fn(g), synonym_tables=_SYNONYM_TABLES)
current_goal_repetition = 0
for t in range(self.cfg.max_episode_length):
prob = self.epsilon if record_trajectory else 0.0
action = agent.step(s, g, env, explore_prob=prob)
s_tp1, r, _, _ = env.step(
action,
record_achieved_goal=False,
goal=p,
atomic_goal=self.cfg.record_atomic_instruction)
s = s_tp1
all_frames.append(
add_text(pad_image(env.render(mode='rgb_array')), g_text))
current_goal_repetition += 1
if record_trajectory:
ep_observation.append(env.get_direct_obs().tolist())
ep_action.append(action)
sample_new_goal = False
if r > env.shape_val:
img = pad_image(env.render(mode='rgb_array'))
for _ in range(5):
all_frames.append(add_text(img, g_text, color='green'))
success += 1
sample_new_goal = True
if current_goal_repetition >= timeout:
all_frames.append(
add_text(pad_image(env.render(mode='rgb_array')), 'time out :('))
timeout_count += 1
sample_new_goal = True
if sample_new_goal:
g, p = env.sample_goal()
if env.all_goals_satisfied:
break
g_text = g
g = self.encode_fn(g_text)
g = np.squeeze(
pad_to_max_length([g], self.cfg.max_sequence_length)[0])
if self._use_synonym_for_rollout and self.cfg.embedding_type != 'random':
g = paraphrase_sentence(
self.decode_fn(g), synonym_tables=_SYNONYM_TABLES)
current_goal_repetition = 0
goal_sampled += 1
# restore the original embedding
if self._use_synonym_for_rollout and self.cfg.embedding_type == 'random':
agent.set_embedding(original_embedding)
print('Rollout finished')
print('{} instrutctions tried given'.format(goal_sampled))
print('{} instructions timed out'.format(timeout_count))
print('{} success rate\n'.format(1 - float(timeout_count) / goal_sampled))
if record_video:
save_video(np.uint8(all_frames), directory, fps=5)
print('Video saved...')
if record_trajectory:
print('Recording trajectory...')
datum = {
'obs': ep_observation,
'action': ep_action,
'achieved goal': ep_agn,
}
save_json(datum, directory[:-4] + '_trajectory.json')
return 1 - float(timeout_count) / goal_sampled