def load_data(path_data, action_space, force_reload=False):
path_data_processed = path_data + ', processed'
file_data_processed = path_data_processed + '/data'
if not force_reload and os.path.exists(file_data_processed):
print(f'load data from {file_data_processed}')
vs = load_vars(file_data_processed)
return vs
print(f'load data from {path_data}')
tools.mkdir(path_data_processed)
files = tools.get_files(path_rel=path_data, sort=True)
# inputs_final, outputs_final = np.zeros((0, 2)), np.zeros((0, 4))
inputs_final, outputs_final = np.zeros((0, 2 * action_space)), np.zeros((0, 4 * action_space))
counts = np.zeros((len(files)), dtype=np.int)
for ind, f in enumerate(files):
mu0s_ats_batch, logsigma0s_batch, ress = load_vars(f)
inputs = np.concatenate((mu0s_ats_batch, logsigma0s_batch), axis=-1)
max_values = np.array([res['max'].x for res in ress])
min_values = np.array([res['min'].x for res in ress])
outputs = np.concatenate((max_values, min_values), axis=-1)
inputs_final = np.concatenate((inputs_final, inputs)) # shape:(None, 2)
outputs_final = np.concatenate((outputs_final, outputs)) # shape:(None, 4)
counts[ind] = mu0s_ats_batch.shape[0]
weights = []
cnt_normalize = counts.mean()
for cnt in counts:
weight = cnt_normalize * 1. / cnt * np.ones(cnt)
weights.append(weight)
weights = np.concatenate(weights, axis=0)
# final = np.concatenate((inputs_final, outputs_final), axis=-1)
# --- delete nan and inf
# final = final[~np.isnan(final).any(axis=1)]
# final = final[~np.isinf(final).any(axis=1)]
inds_reserve = np.logical_and(~np.isnan(outputs_final).any(axis=1), ~np.isinf(outputs_final).any(axis=1))
inputs_final = inputs_final[inds_reserve]
outputs_final = outputs_final[inds_reserve]
weights = weights[inds_reserve]
# --- shuffle
# np.random.shuffle(final)
N = inputs_final.shape[0]
inds_shuffle = np.random.permutation(N)
inputs_final = inputs_final[inds_shuffle]
outputs_final = outputs_final[inds_shuffle]
weights = weights[inds_shuffle]
# inputs_final, outputs_final = np.split(final, indices_or_sections=[2], axis=-1)
ind_split = -500
train_x, train_y, train_weight = \
inputs_final[:ind_split], outputs_final[:ind_split], weights[:ind_split]
eval_x, eval_y, eval_weight = \
inputs_final[ind_split:], outputs_final[ind_split:], weights[ind_split:]
save_vars(file_data_processed, train_x, train_y, train_weight, eval_x, eval_y, eval_weight)
return train_x, train_y, train_weight, eval_x, eval_y, eval_weight
def atari_arg_parser():
"""
Create an argparse.ArgumentParser for run_atari.py.
"""
parser = arg_parser()
parser.add_argument('--env', help='environment ID', default='BreakoutNoFrameskip-v4')
parser.add_argument('--seed', help='RNG seed', type=int, default=0)
parser.add_argument('--num-timesteps', type=int, default=int(10e6))
parser.add_argument('--clipped_type', default='kl2clip', type=str)
parser.add_argument('--use_tabular', default=False, type=ast.literal_eval)
parser.add_argument('--cliprange', default=0.1, type=ast.literal_eval)
parser.add_argument('--delta_kl', default=0.001, type=float)
root_dir_default = '/tmp/baselines'
if not os.path.exists(root_dir_default):
tools.mkdir(root_dir_default)
parser.add_argument('--root_dir', default=root_dir_default, type=str)
parser.add_argument('--sub_dir', default=None, type=str)
parser.add_argument('--force_write', default=1, type=int)
return parser
def load_data_normal(path_data, USE_MULTIPROCESSING=True):
path_save = f'{path_data}/train_preprocessed_reduce_v3'
if os.path.exists(f'{path_save}/data'):
print(f'load data from {path_save}/data')
vs = load_vars(f'{path_save}/data')
return vs
tools.mkdir(f'{path_data}/train_preprocessed')
files = tools.get_files(path_rel=path_data, only_sub=False, sort=False, suffix='.pkl')
actions, deltas, max_mu_logsigma, min_mu_logsigma = [], [], [], []
for ind, f in enumerate(files[:1]):
a_s_batch, _, _, ress_tf = load_vars(f)
actions.append(a_s_batch)
deltas.append(np.ones_like(a_s_batch) * ress_tf.delta)
min_mu_logsigma.append(ress_tf.x.min)
max_mu_logsigma.append(ress_tf.x.max)
actions = np.concatenate(actions, axis=0)
deltas = np.concatenate(deltas, axis=0)
min_mu_logsigma = np.concatenate(min_mu_logsigma, axis=0)
max_mu_logsigma = np.concatenate(max_mu_logsigma, axis=0)
min_mu_tfopt, _ = np.split(min_mu_logsigma, indices_or_sections=2, axis=-1)
max_mu_tfopt, _ = np.split(max_mu_logsigma, indices_or_sections=2, axis=-1)
time0 = time.time()
calculate_mu = get_calculate_mu_func(True)
# TODO: 以下为mu_logsigma_fsolve
if USE_MULTIPROCESSING:
p = multiprocessing.Pool(4)
min_mu_fsolve = p.map(calculate_mu, zip(min_mu_tfopt, actions, deltas))
max_mu_fsolve = p.map(calculate_mu, zip(max_mu_tfopt, actions, deltas))
else:
min_mu_fsolve = list(map(calculate_mu, zip(min_mu_tfopt, actions, deltas)))
max_mu_fsolve = list(map(calculate_mu, zip(max_mu_tfopt, actions, deltas)))
min_mu_fsolve = [_[0] for _ in min_mu_fsolve]
max_mu_fsolve = [_[0] for _ in max_mu_fsolve]
# f_mu_to_logsigma = lambda m, a: (m - a) * (m ** 2 - a * u - 1) / a
time1 = time.time()
print(time1 - time0)
mu_tf_opt = np.concatenate((min_mu_tfopt, max_mu_tfopt), axis=1)
mu_fsolve = np.stack(
(np.concatenate(min_mu_fsolve, axis=0).squeeze(),
np.concatenate(max_mu_fsolve, axis=0).squeeze())
, axis=1)
print(mu_tf_opt - mu_fsolve)
# exit()
inds_shuffle = np.random.permutation(actions.shape[0])
all_ = np.concatenate((actions, deltas, mu_fsolve), axis=1)[inds_shuffle]
all_ = all_[~np.isnan(all_).any(axis=1)]
inputs_all, outputs_all = np.split(all_, indices_or_sections=2,
axis=1) # (actions, deltas) (lambda_min_true, lambda_max_true)
weights = np.ones(shape=(inputs_all.shape[0],))
print(outputs_all.shape)
ind_split = -3000
train_x, train_y, train_weight = \
inputs_all[:ind_split], outputs_all[:ind_split], weights[:ind_split]
eval_x, eval_y, eval_weight = \
inputs_all[ind_split:], outputs_all[ind_split:], weights[ind_split:]
save_vars(f'{path_save}/data', train_x, train_y, train_weight, eval_x, eval_y, eval_weight)
return train_x, train_y, train_weight, eval_x, eval_y, eval_weight,
def main():
args = mujoco_arg_parser().parse_args()
if args.clipped_type == 'kl2clip':
name_tmp = ''
assert (args.cliprange is None) is not (
args.delta_kl is None
), "TRPPO can receive only one of cliprange and delta_kl arguments"
if args.cliprange:
args.kl2clip_clipcontroltype = 'base-clip'
else:
args.kl2clip_clipcontroltype = 'none-clip'
else:
name_tmp = ''
assert args.cliprange, "PPO has to receive a cliprange parameter, the default one is 0.2"
# --- Generate sub_dir of log dir and model dir
split = ','
if args.sub_dir is None:
keys_except = [
'env', 'play', 'root_dir', 'sub_dir', 'force_write', 'lr',
'kl2clip_clipcontroltype'
]
# TODO: tmp for kl2clip_sharelogsigma
keys_fmt = {'num_timesteps': '.0e'}
args_dict = vars(args)
sub_dir = args.env
if not args.clipped_type in ['kl2clip']:
keys_except += ['delta_kl']
if not args.clipped_type in ['origin', 'kl2clip', 'a2c']:
keys_except += ['cliprange']
# --- add keys common
for key in args_dict.keys():
if key not in keys_except and key not in keys_fmt.keys():
sub_dir += f'{split} {key}={args_dict[key]}'
# --- add keys which has specific format
for key in keys_fmt.keys():
sub_dir += f'{split} {key}={args_dict[key]:{keys_fmt[key]}}'
sub_dir += ('' if name_tmp == '' else f'{split} {name_tmp}')
args.sub_dir = sub_dir
tools.mkdir(f'{args.root_dir}/log')
tools.mkdir(f'{args.root_dir}/model')
args.log_dir = f'{args.root_dir}/log/{args.sub_dir}'
args.model_dir = f'{args.root_dir}/model/{args.sub_dir}'
force_write = args.force_write
# Move Dirs
if osp.exists(args.log_dir) or osp.exists(
args.model_dir): # modify name if exist
print(
f"Exsits directory! \n log_dir:'{args.log_dir}' \n model_dir:'{args.model_dir}'\nMove to discard(y or n)?",
end='')
if force_write > 0:
cmd = 'y'
elif force_write < 0:
exit()
else:
cmd = input()
if cmd == 'y':
log_dir_new = args.log_dir.replace('/log/', '/log_discard/')
model_dir_new = args.model_dir.replace('/model/',
'/model_discard/')
import itertools
if osp.exists(log_dir_new) or osp.exists(model_dir_new):
for i in itertools.count():
suffix = f' {split} {i}'
log_dir_new = f'{args.root_dir}/log_discard/{args.sub_dir}{suffix}'
model_dir_new = f'{args.root_dir}/model_discard/{args.sub_dir}{suffix}'
if not osp.exists(log_dir_new) and not osp.exists(
model_dir_new):
break
print(
f"Move log_dir '{args.log_dir}' \n to '{log_dir_new}'. \n"
f"Move model_dir '{args.model_dir}' \n to '{model_dir_new}'"
f"\nConfirm move(y or n)?",
end='')
if force_write > 0:
cmd = 'y'
elif force_write < 0:
exit()
else:
cmd = input()
if cmd == 'y':
import shutil
if osp.exists(args.log_dir):
shutil.move(args.log_dir, log_dir_new)
if osp.exists(args.model_dir):
shutil.move(args.model_dir, model_dir_new)
else:
print("Please Rename 'name_tmp'")
exit()
else:
print("Please Rename 'name_tmp'")
exit()
os.mkdir(args.log_dir)
os.mkdir(args.model_dir)
# exit()
os.mkdir(osp.join(args.model_dir, 'cliprange_max'))
os.mkdir(osp.join(args.model_dir, 'cliprange_min'))
os.mkdir(osp.join(args.model_dir, 'actions'))
os.mkdir(osp.join(args.model_dir, 'mu0_logsigma0'))
os.mkdir(osp.join(args.model_dir, 'kls, ratios'))
os.mkdir(osp.join(args.model_dir, 'advs'))
args_str = vars(args)
with open(f'{args.log_dir}/args.json', 'w') as f:
json.dump(args_str, f, indent=4, separators=(',', ':'))
logger.configure(args.log_dir)
model, env = train(env_id=args.env,
clipped_type=args.clipped_type,
num_timesteps=args.num_timesteps,
seed=args.seed,
args=args)
# model, env = train(args.env, num_timesteps=10, seed=args.seed)
if args.play:
logger.log("Running trained model")
obs = np.zeros((env.num_envs, ) + env.observation_space.shape)
obs[:] = env.reset()
while True:
actions = model.step(obs)[0]
obs[:] = env.step(actions)[0]
env.render()
ratio_mins.append(ratio)
ratio_pre = ratio
else:
ratio_pre = ratio = self.opt_entity2(
pa, delta, 'min', ratio_pre if initialwithpresol else None)
ratio_mins.append(ratio)
ratio_maxs = np.array(ratio_maxs)
ratio_mins = np.array(ratio_mins)
return DotMap(max=ratio_maxs, min=ratio_mins)
import tools_process
path_root_tabular = f'{path_root}/tabular'
tools.mkdir(path_root_tabular)
path_root_tabluar_locker = f'{path_root_tabular}/locker'
tools.mkdir(path_root_tabluar_locker)
class KL2Clip_tabular(object):
def __init__(self):
self.deltas_dict = {}
self._upperbound = 0.99
self._lowerbound = 0.01
def get_tabular(self, delta):
save_path = f'{path_root_tabular}/{delta:.16f}_atari'
if delta in self.deltas_dict:
pass
# TODO: file lock
def main():
parser = atari_arg_parser()
parser.add_argument('--policy',
help='Policy architecture',
choices=['cnn', 'lstm', 'lnlstm', 'mlp'],
default='cnn')
args = parser.parse_args()
if args.clipped_type == 'kl2clip':
name_tmp = ''
if args.cliprange and 'NoFrameskip-v4' not in args.env:
args.kl2clip_clipcontroltype = 'base-clip'
else:
args.kl2clip_clipcontroltype = 'none-clip'
else:
name_tmp = ''
assert args.cliprange, "PPO has to receive a cliprange parameter, the default one is 0.2"
# --- Generate sub_dir of log dir and model dir
split = ','
if args.sub_dir is None:
keys_except = [
'env', 'play', 'root_dir', 'sub_dir', 'force_write', 'lr',
'kl2clip_clipcontroltype'
]
# TODO: tmp for kl2clip_sharelogsigma
keys_fmt = {'num_timesteps': '.0e'}
args_dict = vars(args)
sub_dir = args.env
if not args.clipped_type in ['kl2clip']:
keys_except += ['delta_kl']
if not args.clipped_type in ['origin', 'kl2clip', 'a2c']:
keys_except += ['cliprange']
# --- add keys common
for key in args_dict.keys():
if key not in keys_except and key not in keys_fmt.keys():
sub_dir += f'{split} {key}={args_dict[key]}'
# --- add keys which has specific format
for key in keys_fmt.keys():
sub_dir += f'{split} {key}={args_dict[key]:{keys_fmt[key]}}'
sub_dir += ('' if name_tmp == '' else f'{split} {name_tmp}')
args.sub_dir = sub_dir
tools.mkdir(f'{args.root_dir}/log')
tools.mkdir(f'{args.root_dir}/model')
args.log_dir = f'{args.root_dir}/log/{args.sub_dir}'
args.model_dir = f'{args.root_dir}/model/{args.sub_dir}'
force_write = args.force_write
# Move Dirs
if osp.exists(args.log_dir) or osp.exists(
args.model_dir): # modify name if exist
print(
f"Exsits directory! \n log_dir:'{args.log_dir}' \n model_dir:'{args.model_dir}'\nMove to discard(y or n)?",
end='')
if force_write > 0:
cmd = 'y'
elif force_write < 0:
exit()
else:
cmd = input()
if cmd == 'y':
log_dir_new = args.log_dir.replace('/log/', '/log_discard/')
model_dir_new = args.model_dir.replace('/model/',
'/model_discard/')
import itertools
if osp.exists(log_dir_new) or osp.exists(model_dir_new):
for i in itertools.count():
suffix = f' {split} {i}'
log_dir_new = f'{args.root_dir}/log_discard/{args.sub_dir}{suffix}'
model_dir_new = f'{args.root_dir}/model_discard/{args.sub_dir}{suffix}'
if not osp.exists(log_dir_new) and not osp.exists(
model_dir_new):
break
print(
f"Move log_dir '{args.log_dir}' \n to '{log_dir_new}'. \n"
f"Move model_dir '{args.model_dir}' \n to '{model_dir_new}'"
f"\nConfirm move(y or n)?",
end='')
if force_write > 0:
cmd = 'y'
elif force_write < 0:
exit()
else:
cmd = input()
if cmd == 'y':
import shutil
if osp.exists(args.log_dir):
shutil.move(args.log_dir, log_dir_new)
if osp.exists(args.model_dir):
shutil.move(args.model_dir, model_dir_new)
else:
print("Please Rename 'name_tmp'")
exit()
else:
print("Please Rename 'name_tmp'")
exit()
os.mkdir(args.log_dir)
os.mkdir(args.model_dir)
# exit()
os.mkdir(osp.join(args.model_dir, 'cliprange_max'))
os.mkdir(osp.join(args.model_dir, 'cliprange_min'))
os.mkdir(osp.join(args.model_dir, 'actions'))
# os.mkdir(osp.join(args.model_dir, 'mu0_logsigma0'))
os.mkdir(osp.join(args.model_dir, 'kls, ratios'))
os.mkdir(osp.join(args.model_dir, 'advs'))
args_str = vars(args)
with open(f'{args.log_dir}/args.json', 'w') as f:
json.dump(args_str, f, indent=4, separators=(',', ':'))
logger.configure(args.log_dir)
train(clipped_type=args.clipped_type,
num_timesteps=args.num_timesteps,
seed=args.seed,
args=args,
policy=args.policy)
def prepare_data(dim,
delta,
sharelogsigma,
clipcontroltype,
cliprange,
clip_clipratio,
search_delta=False):
global ress_tf_last
path_data = path_root + '/KL2Clip/data/train_lambda'
Name = f'dim={dim}, delta={delta}, train'
path_data_processed = path_data + f'/{Name}'
tools.mkdir(path_data_processed)
if dim == 1:
logsigma0s = np.array([0])
else:
raise NotImplementedError
logsigma0s = logsigma0s.reshape((-1, dim))
batch_size = 2048
mu = np.zeros((dim, ))
opt = KL2Clip(dim=dim,
batch_size=batch_size,
sharelogsigma=sharelogsigma,
clipcontroltype=clipcontroltype,
cliprange=cliprange)
def get_fn_sample():
mu0 = tf.placeholder(shape=[dim], dtype=tf.float32)
a = tf.placeholder(shape=[batch_size, dim], dtype=tf.float32)
logsigma0 = tf.placeholder(shape=[dim], dtype=tf.float32)
sample_size = tf.placeholder(shape=(), dtype=tf.int32)
dist = DiagGaussianPd(tf.concat((mu0, logsigma0), axis=0))
samples = dist.sample(sample_size)
fn_sample = U.function([mu0, logsigma0, sample_size], samples)
fn_p = U.function([mu0, logsigma0, a], dist.p(a))
return fn_sample, fn_p
sess = U.make_session(make_default=True)
results = []
fn_sample, fn_p = get_fn_sample()
for logsigma0 in logsigma0s:
prefix_save = f'{path_data_processed}/logsigma0={logsigma0}'
Name_f = f"{Name},logsigma0={logsigma0}"
file_fig = f'{prefix_save}.png'
# a_s_batch = fn_sample( mu, logsigma0, batch_size )
a_s_batch = np.linspace(-5, 5, batch_size).reshape((-1, 1))
logsigma0s_batch = np.tile(logsigma0, (batch_size, 1))
print(a_s_batch.max(), a_s_batch.min())
# --- sort the data: have problem in 2-dim
# inds = np.argsort(a_s_batch, axis=0)
# inds = inds.reshape(-1)
# a_s_batch = a_s_batch[inds]
# logsigma0s_batch = logsigma0s_batch[inds]
# tools.reset_time()
# a_s_batch.fill(0)
# print(a_s_batch.shape)
# a_s_batch[0, :]=0
# if search_delta:
# for i in range( batch_size):
# a_s_batch[i,:] = 0.001 * (batch_size-i)
if not os.path.exists(f'{prefix_save}.pkl'):
# ress_tf = opt( mu0_logsigma0_tuple=(a_s_batch, logsigma0s_batch), a=None, delta=delta, clip_clipratio=clip_clipratio)
ress_tf = opt(mu0_logsigma0_tuple=(np.zeros_like(logsigma0s_batch),
logsigma0s_batch),
a=a_s_batch,
delta=delta,
clip_clipratio=clip_clipratio)
print(a_s_batch[0], ress_tf.x.max[0], ress_tf.x.min[0])
save_vars(f'{prefix_save}.pkl', a_s_batch, logsigma0,
logsigma0s_batch, ress_tf)
print(prefix_save)
a_s_batch, logsigma0, logsigma0s_batch, ress_tf = load_vars(
f'{prefix_save}.pkl')
if search_delta:
results.append(ress_tf)
break
if cliprange == clipranges[0]: # TODO tmp
fig = plt.figure(figsize=(20, 10))
markers = ['^', '.']
colors = [['blue', 'red'], ['green', 'hotpink']]
# for ind, opt_name in enumerate(['max']):
for ind, opt_name in enumerate(['max', 'min']):
# if ind == 1:
# continue
# --- plot tensorflow result
ratios, cons = ress_tf.ratio[opt_name], ress_tf.con[opt_name]
print(
f'clip-{opt_name}_mean:{ratios.mean()}, clip-{opt_name}_min:{ratios.min()}, clip-{opt_name}_max:{ratios.max()}'
)
if search_delta:
continue
if DEBUG:
pass
inds_good = cons <= get_ConstraintThreshold(ress_tf.delta)
inds_bad = np.logical_not(inds_good)
if dim == 1:
if ind == 0 and 1:
ps = fn_p(mu, logsigma0, a_s_batch)
# +np.abs(ps.max()) + 1
ratio_new = -np.log(ps)
ratio_new = ratio_new - ratio_new.min() + ratios.min()
alpha = np.exp(-ps * 2)
print(alpha)
# plt.scatter(a_s_batch, ratio_new, s=5, label='ratio_new0')
ratio_new = ratio_new.min() + alpha * (ratio_new -
ratio_new.min())
# plt.scatter( a_s_batch, ratio_new, s=5, label='ratio_new1' )
# ps = -ps
# ratios = ps - ps.min() + ratios.min()
# print( ps.min() )
# ratios_new =np.square( a_s_batch-mu ) * np.exp( -logsigma0 )
# ratio_min = ps / (ps.max()-ps.min()) * ress_tf.ratio.min.max()
# plt.scatter(a_s_batch, ratio_min, s=5, label='square')
# plt.scatter(a_s_batch, 1./ratio_min, s=5, label='square')
# plt.scatter(a_s_batch, 1./ratios, s=5, label='1/max')
def plot_new(alpha):
clip_max_new, clip_min_new = get_clip_new(
alpha,
ress_tf.ratio['max'],
ress_tf.ratio['min'],
clipcontroltype=clipcontroltype)
plt.scatter(a_s_batch,
clip_max_new,
s=5,
label=f'clip_max_{alpha}')
plt.scatter(a_s_batch,
clip_min_new,
s=5,
label=f'clip_min_{alpha}')
if ind == 0:
pass
# plot_new(0.5)
# plot_new(0.5)
# plot_new(-1)
plt.scatter(a_s_batch[inds_good],
ratios[inds_good],
label='ratio_predict-good_' + opt_name,
s=5,
color=colors[ind][0],
marker=markers[ind])
plt.scatter(a_s_batch[inds_bad],
ratios[inds_bad],
label='ratio_predict-bad_' + opt_name,
s=5,
color=colors[ind][1],
marker=markers[ind])
elif dim == 2:
ax = fig.gca(projection='3d')
# ax.view_init(30, 30)
ax.view_init(90, 90)
# ax.plot_trisurf(a_s_batch[:, 0], a_s_batch[:, 1], ratios)
ax.scatter(a_s_batch[inds_good, 0],
a_s_batch[inds_good, 1],
ratios[inds_good],
label='ratio_predict-good_' + opt_name,
s=5,
color=colors[ind][0],
marker=markers[ind])
ax.scatter(a_s_batch[inds_bad, 0],
a_s_batch[inds_bad, 1],
ratios[inds_bad],
label='ratio_predict-bad_' + opt_name,
s=5,
color=colors[ind][1],
marker=markers[ind])
if dim <= 2 and not search_delta:
plt.title(
Name_f +
f'\nstep:{ress_tf.step},rate_satisfycon:{ress_tf.rate_satisfycon_}, rate_statisfydifference_:{ress_tf.rate_statisfydifference_}, difference_max_:{ress_tf.difference_max_}'
)
plt.legend(loc='best')
if not DEBUG:
plt.savefig(file_fig)
opt.close()
if dim <= 2 and not search_delta:
if DEBUG:
if cliprange == clipranges[-1]:
plt_tools.set_postion()
plt.show()
plt.close()