def hir_relabel(self, non_null_future_idx, episode_experience, current_t,
replay_buffer, env):
"""Relabeling trajectories.
Args:
non_null_future_idx: list of time step where something happens
episode_experience: the RL environment
current_t: time time step at which the experience is relabeled
replay_buffer: the experience replay buffer
env: the RL environment
Returns:
the reset state of the environment
"""
ep_len = len(episode_experience)
s, a, _, s_tp1, _, ag = episode_experience[current_t]
if ag:
# TODO(ydjiang): k_immediate logic needs improvement
for _ in range(self.cfg.k_immediate):
ag_text_single = random.choice(ag)
g_type = instruction_type(ag_text_single)
if self.cfg.paraphrase and g_type != 'unary':
ag_text_single = paraphrase_sentence(
ag_text_single,
delete_color=self.cfg.diverse_scene_content)
replay_buffer.add((s, a, env.reward_scale, s_tp1,
self.encode_fn(ag_text_single)))
if g_type == 'unary' and self.cfg.negate_unary:
negative_ag = negate_unary_sentence(ag_text_single)
if negative_ag:
replay_buffer.add(
(s, a, 0.0, s_tp1, self.encode_fn(negative_ag)))
# TODO(ydjiang): repeat logit needs improvement
goal_count, repeat = 0, 0
while goal_count < self.cfg.future_k and repeat < (ep_len -
current_t) * 4:
repeat += 1
future = np.random.randint(current_t, ep_len)
_, _, _, _, _, ag_future = episode_experience[future]
if not ag_future:
continue
random.shuffle(ag_future)
for single_g in ag_future:
if instruction_type(single_g) != 'unary':
discount = self.cfg.discount**(future - current_t)
if self.cfg.paraphrase:
single_g = paraphrase_sentence(
single_g,
delete_color=self.cfg.diverse_scene_content)
replay_buffer.add(
(s, a, discount * env.reward_scale, s_tp1,
self.encode_fn(single_g)))
goal_count += 1
break
def hir_relabel(self, non_null_future_idx, episode_experience, current_t,
replay_buffer, env):
"""Relabeling trajectories.
Args:
non_null_future_idx: list of time step where something happens
episode_experience: the RL environment
current_t: time time step at which the experience is relabeled
replay_buffer: the experience replay buffer
env: the RL environment
Returns:
the reset state of the environment
"""
s, a, _, s_tp1, g, ag = episode_experience[current_t]
if not ag:
return
for _ in range(min(self.cfg.k_immediate, len(ag) + 1)):
ag_text_single = random.choice(ag)
g_type = instruction_type(ag_text_single)
if self.cfg.paraphrase and g_type != 'unary':
ag_text_single = paraphrase_sentence(
ag_text_single,
delete_color=self.cfg.diverse_scene_content)
replay_buffer.add((s, a, env.reward_scale, s_tp1,
self.encode_fn(ag_text_single), ag))
def max_ent_relabel(self, experience, agent, env):
"""Perform maximum entropy relabeling.
Args:
experience: experience to be re-labeled
agent: the RL agent
env: the RL environment
Returns:
relabeled experience
"""
relabel_proportion = self.cfg.relabel_proportion
exp_o = experience[int(len(experience) * relabel_proportion):]
exp_n = experience[:int(len(experience) * relabel_proportion)]
s_o, a_o, r_o, s_tp1_o, g_o, ag_o = [
np.squeeze(elem, axis=1) for elem in np.split(exp_o, 6, 1)
]
s_n, a_n, r_n, s_tp1_n, g_n, ag_n = [
np.squeeze(elem, axis=1) for elem in np.split(exp_n, 6, 1)
]
chosen_q_idx = np.random.choice(np.arange(len(env.all_questions)),
self.cfg.irl_sample_goal_n)
g_candidate = np.array(env.all_questions)[chosen_q_idx]
g_candidate = [q for q, p in g_candidate]
if self.cfg.instruction_repr == 'language':
g_o = np.array(pad_to_max_length(g_o,
self.cfg.max_sequence_length))
if self.cfg.paraphrase:
for i, g_text in enumerate(g_candidate):
g_candidate[i] = paraphrase_sentence(
g_text, delete_color=self.cfg.diverse_scene_content)
g_candidate = [self.encode_fn(g) for g in g_candidate]
g_candidate = np.array(
pad_to_max_length(g_candidate, self.cfg.max_sequence_length))
soft_q = agent.compute_q_over_all_g(s_n, a_n, g_candidate)
normalized_soft_q = tf.nn.softmax(soft_q, axis=-1).numpy()
chosen_g = []
for sq in normalized_soft_q:
chosen_g.append(
np.random.choice(np.arange(sq.shape[0]), 1, p=sq)[0])
g_n = g_candidate[chosen_g]
s = np.concatenate([np.stack(s_o), np.stack(s_n)], axis=0)
a = np.concatenate([a_o, a_n], axis=0)
r = np.concatenate([r_o, r_n], axis=0)
s_tp1 = np.concatenate([np.stack(s_tp1_o), np.stack(s_tp1_n)], axis=0)
g = np.concatenate([g_o, g_n])
if self.cfg.instruction_repr == 'language':
g = np.array(pad_to_max_length(g, self.cfg.max_sequence_length))
return s, a, r, s_tp1, g
def learn(self, env, agent, replay_buffer, **kwargs):
"""Run learning for 1 cycle with consists of num_episode of episodes.
Args:
env: the RL environment
agent: the RL agent
replay_buffer: the experience replay buffer
**kwargs: other potential arguments
Returns:
statistics of the training episode
"""
average_per_ep_reward = []
average_per_ep_achieved_n = []
average_per_ep_relabel_n = []
average_batch_loss = []
curiosity_loss = 0
curr_step = agent.get_global_step()
self.update_epsilon(curr_step)
tic = time.time()
time_rolling_out, time_training = 0.0, 0.0
for _ in range(self.cfg.num_episode):
curr_step = agent.increase_global_step()
sample_new_scene = random.uniform(0, 1) < self.cfg.sample_new_scene_prob
s = self.reset(env, agent, sample_new_scene)
episode_experience = []
episode_reward = 0
episode_achieved_n = 0
episode_relabel_n = 0
# rollout
rollout_tic = time.time()
g_text, p = env.sample_goal()
if env.all_goals_satisfied:
s = self.reset(env, agent, True)
g_text, p = env.sample_goal()
g = np.squeeze(self.encode_fn(g_text))
for t in range(self.cfg.max_episode_length):
a = agent.step(s, g, env, self.epsilon)
s_tp1, r, _, _ = env.step(
a,
record_achieved_goal=True,
goal=p,
atomic_goal=self.cfg.record_atomic_instruction)
ag = env.get_achieved_goals()
ag_text = env.get_achieved_goal_programs()
ag_total = ag # TODO(ydjiang): more can be stored in ag
episode_experience.append((s, a, r, s_tp1, g, ag_total))
episode_reward += r
s = s_tp1
if r > env.shape_val:
episode_achieved_n += 1
g_text, p = env.sample_goal()
if env.all_goals_satisfied:
break
g = np.squeeze(self.encode_fn(g_text))
time_rolling_out += time.time() - rollout_tic
average_per_ep_reward.append(episode_reward)
average_per_ep_achieved_n.append(episode_achieved_n)
# processing trajectory
train_tic = time.time()
episode_length = len(episode_experience)
for t in range(episode_length):
s, a, r, s_tp1, g, ag = episode_experience[t]
episode_relabel_n += float(len(ag) > 0)
g_text = self.decode_fn(g)
if self.cfg.paraphrase:
g_text = paraphrase_sentence(
g_text, delete_color=self.cfg.diverse_scene_content)
g = self.encode_fn(g_text)
replay_buffer.add((s, a, r, s_tp1, g))
if self.cfg.relabeling:
self.hir_relabel(episode_experience, t, replay_buffer, env)
average_per_ep_relabel_n.append(episode_relabel_n / float(episode_length))
# training
if not self.is_warming_up(curr_step):
batch_loss = 0
for _ in range(self.cfg.optimization_steps):
experience = replay_buffer.sample(self.cfg.batchsize)
s, a, r, s_tp1, g = [
np.squeeze(elem, axis=1) for elem in np.split(experience, 5, 1)
]
s = np.stack(s)
s_tp1 = np.stack(s_tp1)
g = np.array(list(g))
if self.cfg.instruction_repr == 'language':
g = np.array(pad_to_max_length(g, self.cfg.max_sequence_length))
batch = {
'obs': np.asarray(s),
'action': np.asarray(a),
'reward': np.asarray(r),
'obs_next': np.asarray(s_tp1),
'g': np.asarray(g)
}
loss_dict = agent.train(batch)
batch_loss += loss_dict['loss']
if 'prediction_loss' in loss_dict:
curiosity_loss += loss_dict['prediction_loss']
average_batch_loss.append(batch_loss / self.cfg.optimization_steps)
time_training += time.time()-train_tic
time_per_episode = (time.time() - tic) / self.cfg.num_episode
time_training_per_episode = time_training / self.cfg.num_episode
time_rolling_out_per_episode = time_rolling_out / self.cfg.num_episode
# Update the target network
agent.update_target_network()
################## Debug ##################
sample = replay_buffer.sample(min(10000, len(replay_buffer.buffer)))
_, _, sample_r, _, _ = [
np.squeeze(elem, axis=1) for elem in np.split(sample, 5, 1)
]
print('n one:', np.sum(np.float32(sample_r == 1.0)), 'n zero',
np.sum(np.float32(sample_r == 0.0)), 'n buff',
len(replay_buffer.buffer))
################## Debug ##################
stats = {
'loss': np.mean(average_batch_loss) if average_batch_loss else 0,
'reward': np.mean(average_per_ep_reward),
'achieved_goal': np.mean(average_per_ep_achieved_n),
'average_relabel_goal': np.mean(average_per_ep_relabel_n),
'epsilon': self.epsilon,
'global_step': curr_step,
'time_per_episode': time_per_episode,
'time_training_per_episode': time_training_per_episode,
'time_rolling_out_per_episode': time_rolling_out_per_episode,
'replay_buffer_reward_avg': np.mean(sample_r),
'replay_buffer_reward_var': np.var(sample_r)
}
return stats
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
def learn(self, env, agent, replay_buffer):
"""Run learning for 1 cycle with consists of num_episode of episodes.
Args:
env: the RL environment
agent: the RL agent
replay_buffer: the experience replay buffer
Returns:
statistics of the training episode
"""
average_per_ep_reward = []
average_per_ep_achieved_n = []
average_per_ep_relabel_n = []
average_batch_loss = []
curr_step = agent.get_global_step()
self.update_epsilon(curr_step)
tic = time.time()
for _ in range(self.cfg.num_episode):
curr_step = agent.increase_global_step()
sample_new_scene = random.uniform(0, 1) < self.cfg.sample_new_scene_prob
s = env.reset(sample_new_scene)
episode_experience = []
episode_reward = 0
episode_achieved_n = 0
episode_relabel_n = 0
# rollout
g_text, p = env.sample_goal()
if env.all_goals_satisfied:
s = env.reset(True)
g_text, p = env.sample_goal()
g = self.encode_fn(g_text)
g = np.squeeze(pad_to_max_length([g], self.cfg.max_sequence_length)[0])
_ = agent.step(s, g, env, 0.0) # taking a step to create weights
for t in range(self.cfg.max_episode_length):
a = agent.step(s, g, env, self.epsilon)
s_tp1, r, _, _ = env.step(
a,
record_achieved_goal=self._use_oracle_instruction,
goal=p,
atomic_goal=self.cfg.record_atomic_instruction)
if self._use_labeler_as_reward:
labeler_answer = self.labeler.verify_instruction(
env.convert_order_invariant_to_direct(s_tp1), g)
r = float(labeler_answer > 0.5)
if self._use_oracle_instruction:
ag = env.get_achieved_goals()
else:
ag = [None]
episode_experience.append((s, a, r, s_tp1, g, ag))
episode_reward += r
s = s_tp1
if r > env.shape_val:
episode_achieved_n += 1
g_text, p = env.sample_goal()
if env.all_goals_satisfied:
break
g = self.encode_fn(g_text)
g = np.squeeze(
pad_to_max_length([g], self.cfg.max_sequence_length)[0])
average_per_ep_reward.append(episode_reward)
average_per_ep_achieved_n.append(episode_achieved_n)
# processing trajectory
episode_length = len(episode_experience)
if not self._use_oracle_instruction: # generate instructions from traj
transition_pair = []
if self.cfg.obs_type == 'order_invariant':
for t in episode_experience:
transition_pair.append([
env.convert_order_invariant_to_direct(t[0]),
env.convert_order_invariant_to_direct(t[3])
])
transition_pair = np.stack(transition_pair)
else:
for t in episode_experience:
transition_pair.append([t[0], t[3]])
all_achieved_goals = self.labeler.label_trajectory(
transition_pair, null_token=2)
for i in range(len(episode_experience)):
s, a, r, s_tp1, g, ag = episode_experience[i]
step_i_text = []
for inst in all_achieved_goals[i]:
decoded_inst = self.decode_fn(inst)
step_i_text.append(decoded_inst)
episode_experience[i] = [s, a, r, s_tp1, g, step_i_text]
non_null_future_idx = [[] for _ in range(episode_length)]
for t in range(episode_length):
_, _, _, _, _, ag = episode_experience[t]
if ag:
for u in range(t):
non_null_future_idx[u].append(t)
for t in range(episode_length):
s, a, r, s_tp1, g, ag = episode_experience[t]
episode_relabel_n += float(len(ag) > 0)
g_text = self.decode_fn(g)
if self.cfg.paraphrase:
g_text = paraphrase_sentence(
g_text, delete_color=self.cfg.diverse_scene_content)
g = self.encode_fn(g_text)
replay_buffer.add((s, a, r, s_tp1, g))
if self.cfg.relabeling:
self.hir_relabel(non_null_future_idx, episode_experience, t,
replay_buffer, env)
average_per_ep_relabel_n.append(episode_relabel_n / float(episode_length))
# training
if not self.is_warming_up(curr_step):
batch_loss = 0
for _ in range(self.cfg.optimization_steps):
experience = replay_buffer.sample(self.cfg.batchsize)
s, a, r, s_tp1, g = [
np.squeeze(elem, axis=1) for elem in np.split(experience, 5, 1)
]
s = np.stack(s)
s_tp1 = np.stack(s_tp1)
g = np.array(list(g))
if self.cfg.instruction_repr == 'language':
g = np.array(pad_to_max_length(g, self.cfg.max_sequence_length))
batch = {
'obs': np.asarray(s),
'action': np.asarray(a),
'reward': np.asarray(r),
'obs_next': np.asarray(s_tp1),
'g': np.asarray(g)
}
loss_dict = agent.train(batch)
batch_loss += loss_dict['loss']
average_batch_loss.append(batch_loss / self.cfg.optimization_steps)
time_per_episode = (time.time() - tic) / self.cfg.num_episode
# Update the target network
agent.update_target_network()
################## Debug ##################
sample = replay_buffer.sample(min(10000, len(replay_buffer.buffer)))
_, _, sample_r, _, _ = [
np.squeeze(elem, axis=1) for elem in np.split(sample, 5, 1)
]
print('n one:', np.sum(np.float32(sample_r == 1.0)), 'n zero',
np.sum(np.float32(sample_r == 0.0)), 'n buff',
len(replay_buffer.buffer))
################## Debug ##################
stats = {
'loss': np.mean(average_batch_loss) if average_batch_loss else 0,
'reward': np.mean(average_per_ep_reward),
'achieved_goal': np.mean(average_per_ep_achieved_n),
'average_relabel_goal': np.mean(average_per_ep_relabel_n),
'epsilon': self.epsilon,
'global_step': curr_step,
'time_per_episode': time_per_episode,
'replay_buffer_reward_avg': np.mean(sample_r),
'replay_buffer_reward_var': np.var(sample_r)
}
return stats
