def run_task(v):
random.seed(v['seed'])
np.random.seed(v['seed'])
# goal generators
logger.log("Initializing the goal generators and the inner env...")
inner_env = normalize(
PointEnv(dim=v['goal_size'], state_bounds=v['state_bounds']))
# inner_env = normalize(PendulumEnv())
center = np.zeros(v['goal_size'])
uniform_goal_generator = UniformStateGenerator(state_size=v['goal_size'],
bounds=v['goal_range'],
center=center)
feasible_goal_ub = np.array(v['state_bounds'])[:v['goal_size']]
# print("the feasible_goal_ub is: ", feasible_goal_ub)
uniform_feasible_goal_generator = UniformStateGenerator(
state_size=v['goal_size'],
bounds=[-1 * feasible_goal_ub, feasible_goal_ub])
env = GoalExplorationEnv(
env=inner_env,
goal_generator=uniform_goal_generator,
obs2goal_transform=lambda x: x[:int(len(x) / 2)],
terminal_eps=v['terminal_eps'],
only_feasible=v['only_feasible'],
distance_metric=v['distance_metric'],
terminate_env=True,
goal_weight=v['goal_weight'],
) # this goal_generator will be updated by a uniform after
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(32, 32),
# Fix the variance since different goals will require different variances, making this parameter hard to learn.
learn_std=v['learn_std'],
adaptive_std=v['adaptive_std'],
std_hidden_sizes=(16, 16),
output_gain=v['output_gain'],
init_std=v['policy_init_std'],
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
n_traj = 3
logger.log("Initializing report and plot_policy_reward...")
log_dir = logger.get_snapshot_dir()
inner_log_dir = osp.join(log_dir, 'inner_iters')
report = HTMLReport(osp.join(log_dir, 'report.html'), images_per_row=3)
report.add_header("{}".format(EXPERIMENT_TYPE))
report.add_text(format_dict(v))
# img = plot_policy_reward(
# policy, env, v['goal_range'],
# horizon=v['horizon'], grid_size=5,
# fname='{}/policy_reward_init.png'.format(log_dir),
# )
# report.add_image(img, 'policy performance initialization\n')
# GAN
logger.log("Instantiating the GAN...")
tf_session = tf.Session()
gan_configs = {key[4:]: value for key, value in v.items() if 'GAN_' in key}
gan = StateGAN(
state_size=v['goal_size'],
evaluater_size=v['num_labels'],
state_range=v['goal_range'],
state_noise_level=v['goal_noise_level'],
generator_layers=v['gan_generator_layers'],
discriminator_layers=v['gan_discriminator_layers'],
noise_size=v['gan_noise_size'],
tf_session=tf_session,
configs=gan_configs,
)
final_gen_loss = 11
k = -1
while final_gen_loss > 10:
k += 1
gan.gan.initialize()
img = plot_gan_samples(gan, v['goal_range'],
'{}/start.png'.format(log_dir))
report.add_image(img, 'GAN re-initialized %i' % k)
logger.log("pretraining the GAN...")
if v['smart_init']:
initial_goals = generate_initial_goals(env,
policy,
v['goal_range'],
horizon=v['horizon'])
if np.size(initial_goals[0]) == 2:
plt.figure()
plt.scatter(initial_goals[:, 0],
initial_goals[:, 1],
marker='x')
plt.xlim(-v['goal_range'], v['goal_range'])
plt.ylim(-v['goal_range'], v['goal_range'])
img = save_image()
report.add_image(
img, 'goals sampled to pretrain GAN: {}'.format(
np.shape(initial_goals)))
dis_loss, gen_loss = gan.pretrain(
initial_goals,
outer_iters=30
# initial_goals, outer_iters=30, generator_iters=10, discriminator_iters=200,
)
final_gen_loss = gen_loss
logger.log("Loss at the end of {}th trial: {}gen, {}disc".format(
k, gen_loss, dis_loss))
else:
gan.pretrain_uniform()
final_gen_loss = 0
logger.log("Plotting GAN samples")
img = plot_gan_samples(gan, v['goal_range'],
'{}/start.png'.format(log_dir))
report.add_image(
img, 'GAN pretrained %i: %i gen_itr, %i disc_itr' %
(k, 10 + k, 200 - k * 10))
# report.add_image(img, 'GAN pretrained %i: %i gen_itr, %i disc_itr' % (k, 10, 200))
report.save()
report.new_row()
all_goals = StateCollection(v['coll_eps'])
logger.log("Starting the outer iterations")
for outer_iter in range(v['outer_iters']):
logger.log("Outer itr # %i" % outer_iter)
# Train GAN
logger.log("Sampling goals...")
raw_goals, _ = gan.sample_states_with_noise(v['num_new_goals'])
if v['replay_buffer'] and outer_iter > 0 and all_goals.size > 0:
# sampler uniformly 2000 old goals and add them to the training pool (50/50)
old_goals = all_goals.sample(v['num_old_goals'])
# print("old_goals: {}, raw_goals: {}".format(old_goals, raw_goals))
goals = np.vstack([raw_goals, old_goals])
else:
# print("no goals in all_goals: sample fresh ones")
goals = raw_goals
logger.log("Evaluating goals before inner training...")
rewards_before = None
if v['num_labels'] == 3:
rewards_before = evaluate_states(goals,
env,
policy,
v['horizon'],
n_traj=n_traj)
logger.log("Perform TRPO with UniformListStateGenerator...")
with ExperimentLogger(inner_log_dir,
'_last',
snapshot_mode='last',
hold_outter_log=True):
# set goal generator to uniformly sample from selected all_goals
env.update_goal_generator(UniformListStateGenerator(
goals.tolist()))
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=v['pg_batch_size'],
max_path_length=v['horizon'],
n_itr=v['inner_iters'],
discount=0.995,
step_size=0.01,
plot=False,
)
algo.train()
# logger.log("Plot performance policy on full grid...")
# img = plot_policy_reward(
# policy, env, v['goal_range'],
# horizon=v['horizon'],
# max_reward=v['max_reward'],
# grid_size=10,
# # fname='{}/policy_reward_{}.png'.format(log_config.plot_dir, outer_iter),
# )
# report.add_image(img, 'policy performance\n itr: {}'.format(outer_iter))
# report.save()
report.add_image(plot_generator_samples(gan, env),
'policy_rewards_{}'.format(outer_iter))
report.add_image(plot_policy_performance(policy, env, v['horizon']),
'gan_samples_{}'.format(outer_iter))
# this re-evaluate the final policy in the collection of goals
logger.log(
"Generating labels by re-evaluating policy on List of goals...")
labels = label_states(
goals,
env,
policy,
v['horizon'],
min_reward=v['min_reward'],
max_reward=v['max_reward'],
old_rewards=rewards_before,
# improvement_threshold=0,
n_traj=n_traj,
)
goal_classes, text_labels = convert_label(labels)
total_goals = labels.shape[0]
goal_class_frac = OrderedDict(
) # this needs to be an ordered dict!! (for the log tabular)
for k in text_labels.keys():
frac = np.sum(goal_classes == k) / total_goals
logger.record_tabular('GenGoal_frac_' + text_labels[k], frac)
goal_class_frac[text_labels[k]] = frac
img = plot_labeled_samples(
samples=goals,
sample_classes=goal_classes,
text_labels=text_labels,
limit=v['goal_range'] + 1,
bounds=env.feasible_goal_space.bounds,
# '{}/sampled_goals_{}.png'.format(log_dir, outer_iter), # if i don't give the file it doesn't save
)
summary_string = ''
for key, value in goal_class_frac.items():
summary_string += key + ' frac: ' + str(value) + '\n'
report.add_image(img,
'itr: {}\nLabels of generated goals:\n{}'.format(
outer_iter, summary_string),
width=500)
# log feasibility of generated goals
feasible = np.array([
1 if env.feasible_goal_space.contains(goal) else 0
for goal in goals
],
dtype=int)
feasibility_rate = np.mean(feasible)
logger.record_tabular('GenGoalFeasibilityRate', feasibility_rate)
img = plot_labeled_samples(
samples=goals,
sample_classes=feasible,
text_labels={
0: 'Infeasible',
1: "Feasible"
},
markers={
0: 'v',
1: 'o'
},
limit=v['goal_range'] + 1,
bounds=env.feasible_goal_space.bounds,
# '{}/sampled_goals_{}.png'.format(log_dir, outer_iter), # if i don't give the file it doesn't save
)
report.add_image(img,
'feasibility of generated goals: {}\n itr: {}'.format(
feasibility_rate, outer_iter),
width=500)
###### try single label for good goals
if v['num_labels'] == 1:
labels = np.logical_and(labels[:, 0],
labels[:, 1]).astype(int).reshape((-1, 1))
logger.log("Training GAN...")
gan.train(
goals,
labels,
v['gan_outer_iters'],
)
logger.log("Evaluating performance on Unif and Fix Goal Gen...")
with logger.tabular_prefix('UnifFeasGoalGen_'):
env.update_goal_generator(uniform_feasible_goal_generator)
evaluate_goal_env(env,
policy=policy,
horizon=v['horizon'],
n_goals=50,
fig_prefix='UnifFeasGoalGen_',
report=report,
n_traj=n_traj)
logger.dump_tabular(with_prefix=False)
report.save()
report.new_row()
# append new goals to list of all goals (replay buffer): Not the low reward ones!!
filtered_raw_goals = [
goal for goal, label in zip(goals, labels) if label[0] == 1
]
all_goals.append(filtered_raw_goals)
def run_task(v):
random.seed(v['seed'])
np.random.seed(v['seed'])
# Log performance of randomly initialized policy with FIXED goal [0.1, 0.1]
logger.log("Initializing report and plot_policy_reward...")
log_dir = logger.get_snapshot_dir() # problem with logger module here!!
report = HTMLReport(osp.join(log_dir, 'report.html'), images_per_row=3)
report.add_header("{}".format(EXPERIMENT_TYPE))
report.add_text(format_dict(v))
tf_session = tf.Session()
inner_env = normalize(AntMazeEnv())
uniform_goal_generator = UniformStateGenerator(state_size=v['goal_size'], bounds=v['goal_range'],
center=v['goal_center'])
env = GoalExplorationEnv(
env=inner_env, goal_generator=uniform_goal_generator,
obs2goal_transform=lambda x: x[-3:-1],
terminal_eps=v['terminal_eps'],
distance_metric=v['distance_metric'],
extend_dist_rew=v['extend_dist_rew'],
only_feasible=v['only_feasible'],
terminate_env=True,
)
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(64, 64),
# Fix the variance since different goals will require different variances, making this parameter hard to learn.
learn_std=v['learn_std'],
adaptive_std=v['adaptive_std'],
std_hidden_sizes=(16, 16), # this is only used if adaptive_std is true!
output_gain=v['output_gain'],
init_std=v['policy_init_std'],
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
# initialize all logging arrays on itr0
outer_iter = 0
logger.log('Generating the Initial Heatmap...')
test_and_plot_policy(policy, env, max_reward=v['max_reward'], sampling_res=sampling_res, n_traj=v['n_traj'],
itr=outer_iter, report=report, limit=v['goal_range'], center=v['goal_center'])
# GAN
logger.log("Instantiating the GAN...")
gan_configs = {key[4:]: value for key, value in v.items() if 'GAN_' in key}
for key, value in gan_configs.items():
if value is tf.train.AdamOptimizer:
gan_configs[key] = tf.train.AdamOptimizer(gan_configs[key + '_stepSize'])
if value is tflearn.initializations.truncated_normal:
gan_configs[key] = tflearn.initializations.truncated_normal(stddev=gan_configs[key + '_stddev'])
gan = StateGAN(
state_size=v['goal_size'],
evaluater_size=v['num_labels'],
state_range=v['goal_range'],
state_center=v['goal_center'],
state_noise_level=v['goal_noise_level'],
generator_layers=v['gan_generator_layers'],
discriminator_layers=v['gan_discriminator_layers'],
noise_size=v['gan_noise_size'],
tf_session=tf_session,
configs=gan_configs,
)
logger.log("pretraining the GAN...")
if v['smart_init']:
feasible_goals = generate_initial_goals(env, policy, v['goal_range'], goal_center=v['goal_center'],
horizon=v['horizon'])
labels = np.ones((feasible_goals.shape[0], 2)).astype(np.float32) # make them all good goals
plot_labeled_states(feasible_goals, labels, report=report, itr=outer_iter,
limit=v['goal_range'], center=v['goal_center'])
dis_loss, gen_loss = gan.pretrain(states=feasible_goals, outer_iters=v['gan_outer_iters'])
print("Loss of Gen and Dis: ", gen_loss, dis_loss)
else:
gan.pretrain_uniform()
# log first samples form the GAN
initial_goals, _ = gan.sample_states_with_noise(v['num_new_goals'])
logger.log("Labeling the goals")
labels = label_states(initial_goals, env, policy, v['horizon'], n_traj=v['n_traj'], key='goal_reached')
plot_labeled_states(initial_goals, labels, report=report, itr=outer_iter,
limit=v['goal_range'], center=v['goal_center'])
report.new_row()
all_goals = StateCollection(distance_threshold=v['coll_eps'])
for outer_iter in range(1, v['outer_iters']):
logger.log("Outer itr # %i" % outer_iter)
# Sample GAN
logger.log("Sampling goals from the GAN")
raw_goals, _ = gan.sample_states_with_noise(v['num_new_goals'])
if v['replay_buffer'] and outer_iter > 0 and all_goals.size > 0:
old_goals = all_goals.sample(v['num_old_goals'])
goals = np.vstack([raw_goals, old_goals])
else:
goals = raw_goals
# if needed label the goals before any update
if v['label_with_variation']:
old_labels, old_rewards = label_states(goals, env, policy, v['horizon'], as_goals=True, n_traj=v['n_traj'],
key='goal_reached', full_path=False, return_rew=True)
# itr_label = outer_iter # use outer_iter to log everything or "last" to log only the last
# with ExperimentLogger(log_dir, itr_label, snapshot_mode='last', hold_outter_log=True):
with ExperimentLogger(log_dir, 'last', snapshot_mode='last', hold_outter_log=True):
logger.log("Updating the environment goal generator")
env.update_goal_generator(
UniformListStateGenerator(
goals.tolist(), persistence=v['persistence'], with_replacement=v['with_replacement'],
)
)
logger.log("Training the algorithm")
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=v['pg_batch_size'],
max_path_length=v['horizon'],
n_itr=v['inner_iters'],
step_size=0.01,
plot=False,
)
trpo_paths = algo.train()
if v['use_trpo_paths']:
logger.log("labeling starts with trpo rollouts")
[goals, labels] = label_states_from_paths(trpo_paths, n_traj=2, key='goal_reached', # using the min n_traj
as_goal=True, env=env)
paths = [path for paths in trpo_paths for path in paths]
elif v['label_with_variation']:
labels, paths = label_states(goals, env, policy, v['horizon'], as_goals=True, n_traj=v['n_traj'],
key='goal_reached', old_rewards=old_rewards, full_path=True)
else:
logger.log("labeling starts manually")
labels, paths = label_states(goals, env, policy, v['horizon'], as_goals=True, n_traj=v['n_traj'],
key='goal_reached', full_path=True)
with logger.tabular_prefix("OnStarts_"):
env.log_diagnostics(paths)
logger.log('Generating the Heatmap...')
test_and_plot_policy(policy, env, max_reward=v['max_reward'], sampling_res=sampling_res, n_traj=v['n_traj'],
itr=outer_iter, report=report, limit=v['goal_range'], center=v['goal_center'])
#logger.log("Labeling the goals")
#labels = label_states(goals, env, policy, v['horizon'], n_traj=v['n_traj'], key='goal_reached')
plot_labeled_states(goals, labels, report=report, itr=outer_iter, limit=v['goal_range'],
center=v['goal_center'], maze_id=v['maze_id'])
# ###### extra for deterministic:
# logger.log("Labeling the goals deterministic")
# with policy.set_std_to_0():
# labels_det = label_states(goals, env, policy, v['horizon'], n_traj=v['n_traj'], n_processes=1)
# plot_labeled_states(goals, labels_det, report=report, itr=outer_iter, limit=v['goal_range'], center=v['goal_center'])
if v['label_with_variation']: # this will use only the performance variation for labeling
labels = np.array(labels[:, -1], dtype=int).reshape((-1, 1))
else:
labels = np.logical_and(labels[:, 0], labels[:, 1]).astype(int).reshape((-1, 1))
logger.log("Training the GAN")
gan.train(
goals, labels,
v['gan_outer_iters'],
)
logger.dump_tabular(with_prefix=False)
report.new_row()
# append new goals to list of all goals (replay buffer): Not the low reward ones!!
filtered_raw_goals = [goal for goal, label in zip(goals, labels) if label[0] == 1]
all_goals.append(filtered_raw_goals)
if v['add_on_policy']:
logger.log("sampling on policy")
feasible_goals = generate_initial_goals(env, policy, v['goal_range'], goal_center=v['goal_center'],
horizon=v['horizon'])
# downsampled_feasible_goals = feasible_goals[np.random.choice(feasible_goals.shape[0], v['add_on_policy']),:]
all_goals.append(feasible_goals)
def run_task(v):
random.seed(v['seed'])
np.random.seed(v['seed'])
sampling_res = 2 if 'sampling_res' not in v.keys() else v['sampling_res']
# Log performance of randomly initialized policy with FIXED goal [0.1, 0.1]
logger.log("Initializing report and plot_policy_reward...")
log_dir = logger.get_snapshot_dir() # problem with logger module here!!
report = HTMLReport(osp.join(log_dir, 'report.html'), images_per_row=3)
report.add_header("{}".format(EXPERIMENT_TYPE))
report.add_text(format_dict(v))
tf_session = tf.Session()
inner_env = normalize(AntMazeEnv(maze_id=v['maze_id']))
uniform_goal_generator = UniformStateGenerator(state_size=v['goal_size'],
bounds=v['goal_range'],
center=v['goal_center'])
env = GoalExplorationEnv(
env=inner_env,
goal_generator=uniform_goal_generator,
obs2goal_transform=lambda x: x[-3:-1],
terminal_eps=v['terminal_eps'],
distance_metric=v['distance_metric'],
extend_dist_rew=v['extend_dist_rew'],
only_feasible=v['only_feasible'],
terminate_env=True,
)
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(64, 64),
# Fix the variance since different goals will require different variances, making this parameter hard to learn.
learn_std=v['learn_std'],
adaptive_std=v['adaptive_std'],
std_hidden_sizes=(16,
16), # this is only used if adaptive_std is true!
output_gain=v['output_gain'],
init_std=v['policy_init_std'],
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
# initialize all logging arrays on itr0
outer_iter = 0
logger.log('Generating the Initial Heatmap...')
test_and_plot_policy(policy,
env,
max_reward=v['max_reward'],
sampling_res=sampling_res,
n_traj=v['n_traj'],
itr=outer_iter,
report=report,
limit=v['goal_range'],
center=v['goal_center'])
report.new_row()
# GAN
logger.log("Instantiating the GAN...")
gan_configs = {key[4:]: value for key, value in v.items() if 'GAN_' in key}
for key, value in gan_configs.items():
if value is tf.train.AdamOptimizer:
gan_configs[key] = tf.train.AdamOptimizer(gan_configs[key +
'_stepSize'])
if value is tflearn.initializations.truncated_normal:
gan_configs[key] = tflearn.initializations.truncated_normal(
stddev=gan_configs[key + '_stddev'])
gan = StateGAN(
state_size=v['goal_size'],
evaluater_size=v['num_labels'],
state_range=v['goal_range'],
state_center=v['goal_center'],
state_noise_level=v['goal_noise_level'],
generator_layers=v['gan_generator_layers'],
discriminator_layers=v['gan_discriminator_layers'],
noise_size=v['gan_noise_size'],
tf_session=tf_session,
configs=gan_configs,
)
all_goals = StateCollection(distance_threshold=v['coll_eps'])
for outer_iter in range(1, v['outer_iters']):
logger.log("Outer itr # %i" % outer_iter)
feasible_goals = generate_initial_goals(env,
policy,
v['goal_range'],
goal_center=v['goal_center'],
horizon=v['horizon'])
labels = np.ones((feasible_goals.shape[0],
2)).astype(np.float32) # make them all good goals
plot_labeled_states(feasible_goals,
labels,
report=report,
itr=outer_iter,
limit=v['goal_range'],
center=v['goal_center'],
maze_id=v['maze_id'],
summary_string_base='On-policy Goals:\n')
if v['only_on_policy']:
goals = feasible_goals[np.random.choice(
feasible_goals.shape[0], v['num_new_goals'], replace=False), :]
else:
logger.log("Training the GAN")
gan.pretrain(feasible_goals, v['gan_outer_iters'])
# Sample GAN
logger.log("Sampling goals from the GAN")
raw_goals, _ = gan.sample_states_with_noise(v['num_new_goals'])
if v['replay_buffer'] and outer_iter > 0 and all_goals.size > 0:
old_goals = all_goals.sample(v['num_old_goals'])
goals = np.vstack([raw_goals, old_goals])
else:
goals = raw_goals
with ExperimentLogger(log_dir,
'last',
snapshot_mode='last',
hold_outter_log=True):
logger.log("Updating the environment goal generator")
env.update_goal_generator(
UniformListStateGenerator(
goals.tolist(),
persistence=v['persistence'],
with_replacement=v['with_replacement'],
))
logger.log("Training the algorithm")
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=v['pg_batch_size'],
max_path_length=v['horizon'],
n_itr=v['inner_iters'],
step_size=0.01,
plot=False,
)
algo.train()
logger.log("Labeling the goals")
labels = label_states(goals,
env,
policy,
v['horizon'],
n_traj=v['n_traj'],
key='goal_reached')
plot_labeled_states(goals,
labels,
report=report,
itr=outer_iter,
limit=v['goal_range'],
center=v['goal_center'],
maze_id=v['maze_id'])
logger.log('Generating the Heatmap...')
test_and_plot_policy(policy,
env,
max_reward=v['max_reward'],
sampling_res=sampling_res,
n_traj=v['n_traj'],
itr=outer_iter,
report=report,
limit=v['goal_range'],
center=v['goal_center'])
logger.dump_tabular(with_prefix=False)
report.new_row()
# append new goals to list of all goals (replay buffer): Not the low reward ones!!
filtered_raw_goals = [
goal for goal, label in zip(goals, labels) if label[0] == 1
]
all_goals.append(filtered_raw_goals)
if v['add_on_policy']:
logger.log("sampling on policy")
feasible_goals = generate_initial_goals(
env,
policy,
v['goal_range'],
goal_center=v['goal_center'],
horizon=v['horizon'])
# downsampled_feasible_goals = feasible_goals[np.random.choice(feasible_goals.shape[0], v['add_on_policy']),:]
all_goals.append(feasible_goals)