def __init__(self,
sess,
env,
reward_model,
dynamics_model,
traj_len=100,
rollout_len=None,
query_loss_opt='pref_uncertainty',
imitation_kwargs={},
use_rand_policy=False,
query_type='sketch',
guided_search=False):
if query_type not in ['pref', 'sketch', 'demo']:
raise ValueError
if traj_len > env.max_ep_len + 1:
traj_len = env.max_ep_len + 1
if rollout_len is None:
rollout_len = env.max_ep_len
if traj_len > rollout_len + 1:
raise ValueError
if use_rand_policy and guided_search:
raise ValueError
if guided_search and query_loss_opt == 'pref_uncertainty':
raise ValueError
self.query_loss = eval('self.%s_query_loss' % query_loss_opt)
self.sess = sess
self.query_type = query_type
self.env = env
self.reward_model = reward_model
self.dynamics_model = dynamics_model
self.traj_len = traj_len
self.rollout_len = rollout_len
self.use_rand_policy = use_rand_policy
self.guided_search = guided_search
self.imitation_kwargs = imitation_kwargs
self.query_loss_opt = query_loss_opt
if self.use_rand_policy:
self.imitator = utils.make_random_policy(self.env)
else:
query_loss_opt_for_pol = (query_loss_opt
if self.guided_search else 'max_rew')
if 'plan_horizon' not in self.imitation_kwargs:
self.imitation_kwargs['plan_horizon'] = self.rollout_len
self.imitator = make_imitation_policy(
self.sess,
self.env,
self.reward_model,
self.dynamics_model,
query_loss_opt=query_loss_opt_for_pol,
**self.imitation_kwargs)
def main():
unused_sess = utils.make_tf_session(gpu_mode=False)
env = envs.make_clfbandit_env(verbose=True)
env.expert_policy = env.make_expert_policy()
env.random_policy = utils.make_random_policy(env)
trans_env = envs.make_clfbandit_trans_env(env)
utils.run_ep(env.expert_policy, env)
utils.run_ep(env.random_policy, env)
utils.run_ep(trans_env.expert_policy, trans_env)
utils.run_ep(trans_env.random_policy, trans_env)
logging.info('OK')
from matplotlib import pyplot as plt
import matplotlib.animation
import matplotlib as mpl
import warnings
warnings.filterwarnings('ignore')
sess = utils.make_tf_session(gpu_mode=False)
plot_traj = lambda traj, *args, **kwargs: utils.plot_trajs([traj], *args, **
kwargs)
env = envs.make_carracing_env(sess, load_reward=True)
random_policy = utils.make_random_policy(env)
trans_env = None
encoder = load_wm_pretrained_vae(sess, env)
dynamics_model = load_wm_pretrained_rnn(encoder, sess, env)
with open(os.path.join(utils.carracing_data_dir, 'rnn_enc_demo_rollouts.pkl'),
'rb') as f:
demo_rollouts = pickle.load(f)
with open(os.path.join(utils.carracing_data_dir, 'rnn_enc_aug_rollouts.pkl'),
'rb') as f:
aug_rollouts = pickle.load(f)
env.default_init_obs = demo_rollouts[-2][50][0]
def main():
sess = utils.make_tf_session(gpu_mode=False)
env = envs.make_carracing_env(sess)
trans_env = envs.make_carracing_trans_env(sess)
random_policy = utils.make_random_policy(env)
utils.run_ep(random_policy, env, max_ep_len=3, render=False)
trans_rollout = utils.run_ep(
random_policy, trans_env, max_ep_len=3, render=False)
logging.info('envs and policies OK')
raw_demo_rollouts = [
utils.run_ep(random_policy, env, max_ep_len=3, render=False)
for _ in range(n_demo_rollouts)
]
raw_aug_rollouts = [
utils.run_ep(random_policy, env, max_ep_len=3, render=False)
for _ in range(n_aug_rollouts)
]
raw_aug_rollouts += raw_demo_rollouts
raw_aug_obses = []
for rollout in raw_aug_rollouts:
for x in rollout:
raw_aug_obses.append(x[0])
raw_aug_obses = np.array(raw_aug_obses)
raw_aug_obs_data = utils.split_rollouts({'obses': raw_aug_obses})
logging.info('data collection OK')
encoder = VAEModel(
sess,
env,
learning_rate=0.0001,
kl_tolerance=0.5,
scope=str(uuid.uuid4()),
scope_file=os.path.join(test_data_dir, 'enc_scope.pkl'),
tf_file=os.path.join(test_data_dir, 'enc.tf'))
encoder.train(
raw_aug_obs_data,
iterations=1,
ftol=1e-4,
learning_rate=1e-3,
val_update_freq=1,
verbose=False)
encoder = load_wm_pretrained_vae(sess, env)
encoder.save()
encoder.load()
obs = raw_aug_rollouts[0][0][0]
latent = encoder.encode_frame(obs)
unused_recon = encoder.decode_latent(latent)
logging.info('encoder OK')
raw_aug_traj_data = utils.split_rollouts(
utils.vectorize_rollouts(
raw_aug_rollouts, env.max_ep_len, preserve_trajs=True))
abs_model = AbsorptionModel(
sess,
env,
n_layers=1,
layer_size=32,
scope=str(uuid.uuid4()),
scope_file=os.path.join(test_data_dir, 'abs_scope.pkl'),
tf_file=os.path.join(test_data_dir, 'abs.tf'))
dynamics_model = MDNRNNDynamicsModel(
encoder,
sess,
env,
scope=str(uuid.uuid4()),
tf_file=os.path.join(test_data_dir, 'dyn.tf'),
scope_file=os.path.join(test_data_dir, 'dyn_scope.pkl'),
abs_model=abs_model)
dynamics_model.train(
raw_aug_traj_data,
iterations=1,
learning_rate=1e-3,
ftol=1e-4,
batch_size=2,
val_update_freq=1,
verbose=False)
dynamics_model = load_wm_pretrained_rnn(encoder, sess, env)
dynamics_model.save()
dynamics_model.load()
demo_traj_data = utils.rnn_encode_rollouts(raw_demo_rollouts, env, encoder,
dynamics_model)
aug_traj_data = utils.rnn_encode_rollouts(raw_aug_rollouts, env, encoder,
dynamics_model)
demo_rollouts = utils.rollouts_of_traj_data(demo_traj_data)
aug_rollouts = utils.rollouts_of_traj_data(aug_traj_data)
demo_data = utils.split_rollouts(utils.flatten_traj_data(demo_traj_data))
aug_data = utils.split_rollouts(utils.flatten_traj_data(aug_traj_data))
env.default_init_obs = aug_rollouts[0][0][0]
trans_rollouts = utils.rollouts_of_traj_data(
utils.rnn_encode_rollouts([trans_rollout], trans_env, encoder,
dynamics_model))
trans_env.default_init_obs = trans_rollouts[0][0][0]
logging.info('mdnrnn dynamics OK')
demo_data_for_reward_model = demo_data
demo_rollouts_for_reward_model = demo_rollouts
sketch_data_for_reward_model = aug_data
sketch_rollouts_for_reward_model = aug_rollouts
reward_init_kwargs = {
'n_rew_nets_in_ensemble': 2,
'n_layers': 1,
'layer_size': 32,
'scope': str(uuid.uuid4()),
'scope_file': os.path.join(test_data_dir, 'true_rew_scope.pkl'),
'tf_file': os.path.join(test_data_dir, 'true_rew.tf'),
'rew_func_input': "s'",
'use_discrete_rewards': True
}
reward_train_kwargs = {
'demo_coeff': 1.,
'sketch_coeff': 1.,
'iterations': 1,
'ftol': 1e-4,
'batch_size': 2,
'learning_rate': 1e-3,
'val_update_freq': 1,
'verbose': False
}
data = envs.make_carracing_rew(
sess,
env,
sketch_data=sketch_data_for_reward_model,
reward_init_kwargs=reward_init_kwargs,
reward_train_kwargs=reward_train_kwargs)
env.__dict__.update(data)
trans_env.__dict__.update(data)
autolabels = reward_models.autolabel_prefs(
aug_rollouts, env, segment_len=env.max_ep_len + 1)
pref_logs_for_reward_model = autolabels
pref_data_for_reward_model = utils.split_prefs(autolabels)
logging.info('autolabels OK')
for rew_func_input in ['s', 'sa', "s'"]:
reward_model = reward_models.RewardModel(
sess,
env,
n_rew_nets_in_ensemble=2,
n_layers=1,
layer_size=32,
scope=str(uuid.uuid4()),
scope_file=os.path.join(test_data_dir, 'rew_scope.pkl'),
tf_file=os.path.join(test_data_dir, 'rew.tf'),
rew_func_input=rew_func_input,
use_discrete_rewards=True)
for demo_data in [None, demo_data_for_reward_model]:
for sketch_data in [None, sketch_data_for_reward_model]:
for pref_data in [None, pref_data_for_reward_model]:
if pref_data is None and sketch_data is None:
continue
reward_model.train(
demo_data=demo_data,
sketch_data=sketch_data,
pref_data=pref_data,
demo_coeff=1.,
sketch_coeff=1.,
iterations=1,
ftol=1e-4,
batch_size=2,
learning_rate=1e-3,
val_update_freq=1,
verbose=False)
reward_model.save()
reward_model.load()
logging.info('reward models OK')
for query_loss_opt in [
'pref_uncertainty', 'rew_uncertainty', 'max_rew', 'min_rew', 'max_nov'
]:
for init_obs in [None, env.default_init_obs]:
for join_trajs_at_init_state in [True, False]:
for shoot_steps in [1, 2]:
if (shoot_steps > 1 and
np.array(init_obs == env.default_init_obs).all()):
continue
traj_opt = GDTrajOptimizer(
sess,
env,
reward_model,
dynamics_model,
traj_len=2,
n_trajs=2,
prior_coeff=1.,
diversity_coeff=0.,
query_loss_opt=query_loss_opt,
opt_init_obs=(init_obs is None),
join_trajs_at_init_state=join_trajs_at_init_state,
shoot_steps=shoot_steps,
learning_rate=1e-2)
traj_opt.run(
init_obs=init_obs,
iterations=1,
ftol=1e-4,
verbose=False,
)
logging.info('grad descent traj opt OK')
imitation_kwargs = {'plan_horizon': 10, 'n_blind_steps': 2, 'test_mode': True}
for n_eval_rollouts in [0, 1]:
reward_models.evaluate_reward_model(
sess,
env,
trans_env,
reward_model,
dynamics_model,
offpol_eval_rollouts=sketch_rollouts_for_reward_model,
n_eval_rollouts=n_eval_rollouts,
imitation_kwargs=imitation_kwargs)
logging.info('reward eval OK')
for query_loss_opt in [
'pref_uncertainty', 'rew_uncertainty', 'max_rew', 'min_rew', 'max_nov',
'unif'
]:
for use_rand_policy in [False, True]:
traj_opt = StochTrajOptimizer(
sess,
env,
reward_model,
dynamics_model,
traj_len=2,
rollout_len=2,
query_loss_opt=query_loss_opt,
use_rand_policy=use_rand_policy)
for init_obs in [None, env.default_init_obs]:
traj_opt.run(n_trajs=2, n_samples=2, init_obs=init_obs, verbose=False)
logging.info('stoch traj opt OK')
reward_model = reward_models.RewardModel(
sess,
env,
n_rew_nets_in_ensemble=2,
n_layers=1,
layer_size=32,
scope=str(uuid.uuid4()),
scope_file=os.path.join(test_data_dir, 'rew_scope.pkl'),
tf_file=os.path.join(test_data_dir, 'rew.tf'),
rew_func_input="s'",
use_discrete_rewards=True)
rew_optimizer = InteractiveRewardOptimizer(sess, env, trans_env, reward_model,
dynamics_model)
reward_train_kwargs = {
'demo_coeff': 1.,
'sketch_coeff': 1.,
'iterations': 1,
'ftol': 1e-4,
'batch_size': 2,
'learning_rate': 1e-3,
'val_update_freq': 1,
'verbose': False
}
dynamics_train_kwargs = {
'iterations': 1,
'batch_size': 2,
'learning_rate': 1e-3,
'ftol': 1e-4,
'val_update_freq': 1,
'verbose': False
}
gd_traj_opt_init_kwargs = {
'traj_len': env.max_ep_len,
'n_trajs': 2,
'prior_coeff': 1.,
'diversity_coeff': 1.,
'query_loss_opt': 'pref_uncertainty',
'opt_init_obs': False,
'learning_rate': 1e-2,
'join_trajs_at_init_state': False
}
gd_traj_opt_run_kwargs = {
'init_obs': env.default_init_obs,
'iterations': 1,
'ftol': 1e-4,
'verbose': False,
}
unused_stoch_traj_opt_init_kwargs = {
'traj_len': 2,
'rollout_len': 2,
'query_loss_opt': 'pref_uncertainty'
}
unused_stoch_traj_opt_run_kwargs = {
'n_samples': 2,
'init_obs': None,
'verbose': False
}
eval_kwargs = {'n_eval_rollouts': 1}
for init_train in [True, False]:
for query_type in ['pref', 'sketch']:
rew_optimizer.run(
demo_rollouts=demo_rollouts_for_reward_model,
sketch_rollouts=sketch_rollouts_for_reward_model,
pref_logs=pref_logs_for_reward_model,
rollouts_for_dyn=raw_aug_rollouts,
reward_train_kwargs=reward_train_kwargs,
dynamics_train_kwargs=dynamics_train_kwargs,
traj_opt_cls=GDTrajOptimizer,
traj_opt_run_kwargs=gd_traj_opt_run_kwargs,
traj_opt_init_kwargs=gd_traj_opt_init_kwargs,
imitation_kwargs=imitation_kwargs,
eval_kwargs=eval_kwargs,
init_train_dyn=init_train,
init_train_rew=init_train,
n_imitation_rollouts_per_dyn_update=1,
n_queries=1,
reward_update_freq=1,
reward_eval_freq=1,
dyn_update_freq=1,
verbose=False,
query_type=query_type)
rew_optimizer.save()
rew_optimizer.load()
logging.info('rqst OK')
def main():
sess = utils.make_tf_session(gpu_mode=False)
env = envs.make_pointmass_env()
trans_env = envs.make_pointmass_trans_env(env)
expert_policy = env.make_expert_policy()
random_policy = utils.make_random_policy(env)
default_init_obs = env.default_init_obs
utils.run_ep(expert_policy, env)
utils.run_ep(random_policy, env)
utils.run_ep(expert_policy, trans_env)
utils.run_ep(random_policy, trans_env)
logging.info('envs and policies OK')
demo_rollouts = [
utils.run_ep(expert_policy, env) for _ in range(n_demo_rollouts)
]
aug_rollouts = demo_rollouts + [
utils.run_ep(random_policy, env) for _ in range(n_aug_rollouts)
]
demo_data = utils.split_rollouts(
utils.vectorize_rollouts(demo_rollouts, env.max_ep_len))
aug_data = utils.split_rollouts(
utils.vectorize_rollouts(aug_rollouts, env.max_ep_len))
unused_demo_traj_data = utils.split_rollouts(
utils.vectorize_rollouts(demo_rollouts,
env.max_ep_len,
preserve_trajs=True))
unused_aug_traj_data = utils.split_rollouts(
utils.vectorize_rollouts(aug_rollouts,
env.max_ep_len,
preserve_trajs=True))
logging.info('data collection OK')
abs_model = AbsorptionModel(sess,
env,
n_layers=1,
layer_size=32,
scope=str(uuid.uuid4()),
scope_file=os.path.join(
test_data_dir, 'abs_scope.pkl'),
tf_file=os.path.join(test_data_dir, 'abs.tf'))
dynamics_model = MLPDynamicsModel(
sess,
env,
n_layers=1,
layer_size=32,
scope=str(uuid.uuid4()),
scope_file=os.path.join(test_data_dir, 'dyn_scope.pkl'),
tf_file=os.path.join(test_data_dir, 'dyn.tf'),
abs_model=abs_model)
dynamics_model.train(aug_data,
iterations=1,
ftol=1e-4,
learning_rate=1e-3,
batch_size=4,
val_update_freq=1,
verbose=False)
dynamics_model.save()
dynamics_model.load()
logging.info('dynamics model OK')
demo_data_for_reward_model = demo_data
demo_rollouts_for_reward_model = demo_rollouts
sketch_data_for_reward_model = aug_data
sketch_rollouts_for_reward_model = aug_rollouts
autolabels = reward_models.autolabel_prefs(
sketch_rollouts_for_reward_model, env, segment_len=env.max_ep_len + 1)
pref_logs_for_reward_model = autolabels
pref_data_for_reward_model = utils.split_prefs(autolabels)
logging.info('autolabels OK')
for rew_func_input in ['sa', 's', "s'"]:
reward_model = reward_models.RewardModel(
sess,
env,
n_rew_nets_in_ensemble=4,
n_layers=1,
layer_size=64,
scope=str(uuid.uuid4()),
scope_file=os.path.join(test_data_dir, 'rew_scope.pkl'),
tf_file=os.path.join(test_data_dir, 'rew.tf'),
rew_func_input=rew_func_input,
use_discrete_rewards=True)
for demo_data in [None, demo_data_for_reward_model]:
for sketch_data in [None, sketch_data_for_reward_model]:
for pref_data in [None, pref_data_for_reward_model]:
if pref_data is None and sketch_data is None:
continue
reward_model.train(demo_data=demo_data,
sketch_data=sketch_data,
pref_data=pref_data,
demo_coeff=1.,
sketch_coeff=1.,
iterations=1,
ftol=1e-4,
batch_size=4,
learning_rate=1e-3,
val_update_freq=1,
verbose=False)
reward_model.save()
reward_model.load()
logging.info('reward models OK')
for query_loss_opt in [
'pref_uncertainty', 'rew_uncertainty', 'max_rew', 'min_rew',
'max_nov'
]:
for init_obs in [None, default_init_obs]:
for join_trajs_at_init_state in [True, False]:
for query_type in ['pref', 'demo', 'sketch']:
if query_type == 'pref' and query_loss_opt == 'max_nov':
continue
for shoot_steps in [1, 2]:
traj_optimizer = GDTrajOptimizer(
sess,
env,
reward_model,
dynamics_model,
traj_len=2,
n_trajs=2,
prior_coeff=1.,
diversity_coeff=0.,
query_loss_opt=query_loss_opt,
opt_init_obs=(init_obs is None),
join_trajs_at_init_state=join_trajs_at_init_state,
shoot_steps=shoot_steps,
learning_rate=1e-2,
query_type=query_type)
traj_optimizer.run(
init_obs=init_obs,
iterations=1,
ftol=1e-4,
verbose=False,
)
logging.info('grad descent traj opt OK')
imitation_kwargs = {
'plan_horizon': 10,
'n_blind_steps': 2,
'test_mode': True
}
for n_eval_rollouts in [0, 1]:
reward_models.evaluate_reward_model(
sess,
env,
trans_env,
reward_model,
dynamics_model,
offpol_eval_rollouts=sketch_rollouts_for_reward_model,
n_eval_rollouts=n_eval_rollouts,
imitation_kwargs=imitation_kwargs)
logging.info('reward eval OK')
for query_loss_opt in [
'pref_uncertainty', 'rew_uncertainty', 'max_rew', 'min_rew',
'max_nov', 'unif'
]:
for use_rand_policy in [False, True]:
traj_optimizer = StochTrajOptimizer(
sess,
env,
reward_model,
dynamics_model,
traj_len=2,
rollout_len=2,
query_loss_opt=query_loss_opt,
use_rand_policy=use_rand_policy)
for init_obs in [None, default_init_obs]:
traj_optimizer.run(n_trajs=2,
n_samples=2,
init_obs=init_obs,
verbose=False)
logging.info('stoch traj opt OK')
reward_model = reward_models.RewardModel(
sess,
env,
n_rew_nets_in_ensemble=4,
n_layers=1,
layer_size=64,
scope=str(uuid.uuid4()),
scope_file=os.path.join(test_data_dir, 'rew_scope.pkl'),
tf_file=os.path.join(test_data_dir, 'rew.tf'),
use_discrete_rewards=True)
rew_optimizer = InteractiveRewardOptimizer(sess, env, trans_env,
reward_model, dynamics_model)
reward_train_kwargs = {
'demo_coeff': 1.,
'sketch_coeff': 1.,
'iterations': 1,
'ftol': 1e-4,
'batch_size': 4,
'learning_rate': 1e-3,
'val_update_freq': 1,
'verbose': False
}
dynamics_train_kwargs = {
'iterations': 1,
'batch_size': 4,
'learning_rate': 1e-3,
'ftol': 1e-4,
'val_update_freq': 1,
'verbose': False
}
gd_traj_opt_init_kwargs = {
'traj_len': env.max_ep_len,
'n_trajs': 2,
'prior_coeff': 1.,
'diversity_coeff': 1.,
'query_loss_opt': 'pref_uncertainty',
'opt_init_obs': False,
'learning_rate': 1e-2,
'join_trajs_at_init_state': False
}
gd_traj_opt_run_kwargs = {
'init_obs': default_init_obs,
'iterations': 1,
'ftol': 1e-4,
'verbose': False
}
unused_stoch_traj_opt_init_kwargs = {
'traj_len': 2,
'rollout_len': 2,
'query_loss_opt': 'pref_uncertainty'
}
unused_stoch_traj_opt_run_kwargs = {
'n_samples': 2,
'init_obs': None,
'verbose': False
}
eval_kwargs = {'n_eval_rollouts': 1}
for init_train in [True, False]:
for query_type in ['pref', 'sketch']:
rew_optimizer.run(demo_rollouts=demo_rollouts_for_reward_model,
sketch_rollouts=sketch_rollouts_for_reward_model,
pref_logs=pref_logs_for_reward_model,
rollouts_for_dyn=aug_rollouts,
reward_train_kwargs=reward_train_kwargs,
dynamics_train_kwargs=dynamics_train_kwargs,
traj_opt_cls=GDTrajOptimizer,
traj_opt_run_kwargs=gd_traj_opt_run_kwargs,
traj_opt_init_kwargs=gd_traj_opt_init_kwargs,
imitation_kwargs=imitation_kwargs,
eval_kwargs=eval_kwargs,
init_train_dyn=init_train,
init_train_rew=init_train,
n_imitation_rollouts_per_dyn_update=1,
n_queries=1,
reward_update_freq=1,
reward_eval_freq=1,
dyn_update_freq=1,
verbose=False,
query_type=query_type)
rew_optimizer.save()
rew_optimizer.load()
logging.info('rqst OK')
def _run(
self,
init_obs=None,
act_seq=None,
iterations=10000,
ftol=1e-6,
min_iters=2,
verbose=False,
warm_start=False,
init_with_lbfgs=False,
init_act_seq=None,
init_traj=None,
):
if (init_obs is not None) == self.opt_init_obs:
raise ValueError
if (act_seq is not None) == self.opt_act_seq:
raise ValueError
if act_seq is not None and init_act_seq is not None:
raise ValueError
if init_act_seq is not None and warm_start:
raise ValueError
if self.query_loss_opt == 'unif':
if self.env.name == 'clfbandit':
std = np.exp(-self.prior_coeff)
def rand_traj():
obs = np.random.normal(0, std,
self.env.n_z_dim)[np.newaxis, :]
next_obs = self.env.absorbing_state[np.newaxis, :]
return np.concatenate((obs, next_obs), axis=0)
trajs_eval = [rand_traj() for _ in range(self.n_trajs)]
act_seqs_eval = [[self.env.action_space.sample()]
for _ in range(self.n_trajs)]
elif self.env.name == 'pointmass':
unif_env = envs.make_pointmass_env()
random_policy = utils.make_random_policy(unif_env)
unif_rollouts = [
utils.run_ep(random_policy, unif_env, max_ep_len=1)
for _ in range(self.n_trajs)
]
trajs_eval = [
utils.traj_of_rollout(rollout) for rollout in unif_rollouts
]
act_seqs_eval = [
utils.act_seq_of_rollout(rollout)
for rollout in unif_rollouts
]
else:
raise ValueError
loss_eval = 0.
return {
'traj': trajs_eval,
'act_seq': act_seqs_eval,
'loss': loss_eval
}
scopes = [self.opt_scope]
if not warm_start:
scopes.append(self.traj_scope)
utils.init_tf_vars(self.sess, scopes, use_cache=True)
feed_dict = {}
assign_ops = []
if init_act_seq is not None:
feed_dict[self.init_act_seq_ph] = init_act_seq
assign_ops.append(self.assign_init_act_seq)
if init_traj is not None:
self.obs_dim = (self.env.n_z_dim if self.env.name == 'carracing'
else self.env.n_obs_dim)
feed_dict[self.init_traj_ph] = init_traj[1:, :self.obs_dim]
assign_ops.append(self.assign_init_traj)
if assign_ops != []:
self.sess.run(assign_ops, feed_dict=feed_dict)
feed_dict = {}
if init_obs is not None:
feed_dict[self.init_obs_ph] = init_obs() if callable(
init_obs) else init_obs
if act_seq is not None:
feed_dict[self.act_seq_ph] = act_seq
if verbose:
print('iters loss')
if init_with_lbfgs:
self.lbfgs_optimizer.minimize(self.sess, feed_dict=feed_dict)
loss_evals = []
loss_eval, trajs_eval, act_seqs_eval = self.sess.run(
[self.loss, self.trajs, self.act_seqs], feed_dict=feed_dict)
best_eval = {
'traj': trajs_eval,
'act_seq': act_seqs_eval,
'loss': loss_eval
}
#start_time = time.time() # uncomment for profiling
for t in range(iterations):
loss_eval, trajs_eval, act_seqs_eval, _ = self.sess.run(
[self.loss, self.trajs, self.act_seqs, self.update_op],
feed_dict=feed_dict)
if verbose:
print('%d %f' % (t, loss_eval))
loss_evals.append(loss_eval)
if loss_eval < best_eval['loss']:
best_eval = {
'traj': trajs_eval,
'act_seq': act_seqs_eval,
'loss': loss_eval
}
if ftol is not None and utils.converged(
loss_evals, ftol, min_iters=min_iters):
break
# uncomment for profiling
#print('call to update_op: %0.3f' % ((time.time() - start_time) / t))
#print('iterations: %d' % t)
if verbose:
plt.plot(loss_evals)
plt.show()
return best_eval