def __init__(self, epsilon=1e-4, shape=(), scope=''):
sess = get_session()
self._new_mean = tf.placeholder(shape=shape, dtype=tf.float64)
self._new_var = tf.placeholder(shape=shape, dtype=tf.float64)
self._new_count = tf.placeholder(shape=(), dtype=tf.float64)
with tf.variable_scope(scope, reuse=tf.AUTO_REUSE):
self._mean = tf.get_variable('mean',
initializer=np.zeros(
shape, 'float64'),
dtype=tf.float64)
self._var = tf.get_variable('std',
initializer=np.ones(shape, 'float64'),
dtype=tf.float64)
self._count = tf.get_variable('count',
initializer=np.full((), epsilon,
'float64'),
dtype=tf.float64)
self.update_ops = tf.group([
self._var.assign(self._new_var),
self._mean.assign(self._new_mean),
self._count.assign(self._new_count)
])
sess.run(tf.variables_initializer([self._mean, self._var,
self._count]))
self.sess = sess
self._set_mean_var_count()
def profile_tf_runningmeanstd():
import time
from baseline.common import tf_util
tf_util.get_session(config=tf.ConfigProto(inter_op_parallelism_threads=1,
intra_op_parallelism_threads=1,
allow_soft_placement=True))
x = np.random.random((376, ))
n_trials = 10000
rms = RunningMeanStd()
tfrms = TfRunningMeanStd()
tic1 = time.time()
for _ in range(n_trials):
rms.update(x)
tic2 = time.time()
for _ in range(n_trials):
tfrms.update(x)
tic3 = time.time()
print('rms update time ({} trials): {} s'.format(n_trials, tic2 - tic1))
print('tfrms update time ({} trials): {} s'.format(n_trials, tic3 - tic2))
tic1 = time.time()
for _ in range(n_trials):
z1 = rms.mean
tic2 = time.time()
for _ in range(n_trials):
z2 = tfrms.mean
assert z1 == z2
tic3 = time.time()
print('rms get mean time ({} trials): {} s'.format(n_trials, tic2 - tic1))
print('tfrms get mean time ({} trials): {} s'.format(
n_trials, tic3 - tic2))
'''
def test_nonfreeze():
np.random.seed(0)
tf.set_random_seed(0)
a = tf.Variable(np.random.randn(3).astype('float32'))
b = tf.Variable(np.random.randn(2, 5).astype('float32'))
loss = tf.reduce_sum(tf.square(a)) + tf.reduce_sum(tf.sin(b))
stepsize = 1e-2
# for some reason the session config with inter_op_parallelism_threads was causing
# nested sess.run calls to freeze
config = tf.ConfigProto(inter_op_parallelism_threads=1)
sess = U.get_session(config=config)
update_op = MpiAdamOptimizer(comm=MPI.COMM_WORLD,
learning_rate=stepsize).minimize(loss)
sess.run(tf.global_variables_initializer())
losslist_ref = []
for i in range(100):
l, _ = sess.run([loss, update_op])
print(i, l)
losslist_ref.append(l)
def __init__(self,
policy,
env,
nsteps,
ent_coef=0.01,
vf_coef=0.5,
max_grad_norm=0.5,
lr=7e-4,
alpha=0.99,
epsilon=1e-5,
total_timesteps=int(80e6),
lrschedule='linear'):
sess = tf_util.get_session()
nenvs = env.num_envs
nbatch = nenvs * nsteps
with tf.variable_scope('a2c_model', reuse=tf.AUTO_REUSE):
# step_model is used for sampling
step_model = policy(nenvs, 1, sess)
# train_model is used to train our network
train_model = policy(nbatch, nsteps, sess)
A = tf.placeholder(train_model.action.dtype, train_model.action.shape)
ADV = tf.placeholder(tf.float32, [nbatch])
R = tf.placeholder(tf.float32, [nbatch])
LR = tf.placeholder(tf.float32, [])
# Calculate the loss
# Total loss = Policy gradient loss - entropy * entropy coefficient + Value coefficient * value loss
# Policy loss
neglogpac = train_model.pd.neglogp(A)
# L = A(s,a) * -logpi(a|s)
pg_loss = tf.reduce_mean(ADV * neglogpac)
# Entropy is used to improve exploration by limiting the premature convergence to suboptimal policy.
entropy = tf.reduce_mean(train_model.pd.entropy())
# Value loss
vf_loss = losses.mean_squared_error(tf.squeeze(train_model.vf), R)
loss = pg_loss - entropy * ent_coef + vf_loss * vf_coef
# Update parameters using loss
# 1. Get the model parameters
params = find_trainable_variables("a2c_model")
# 2. Calculate the gradients
grads = tf.gradients(loss, params)
if max_grad_norm is not None:
# Clip the gradients (normalize)
grads, grad_norm = tf.clip_by_global_norm(grads, max_grad_norm)
grads = list(zip(grads, params))
# zip aggregate each gradient with parameters associated
# For instance zip(ABCD, xyza) => Ax, By, Cz, Da
# 3. Make op for one policy and value update step of A2C
trainer = tf.train.RMSPropOptimizer(learning_rate=LR,
decay=alpha,
epsilon=epsilon)
_train = trainer.apply_gradients(grads)
lr = Scheduler(v=lr, nvalues=total_timesteps, schedule=lrschedule)
def train(obs, states, rewards, masks, actions, values):
# Here we calculate advantage A(s,a) = R + yV(s') - V(s)
# rewards = R + yV(s')
advs = rewards - values
for step in range(len(obs)):
cur_lr = lr.value()
td_map = {
train_model.X: obs,
A: actions,
ADV: advs,
R: rewards,
LR: cur_lr
}
if states is not None:
td_map[train_model.S] = states
td_map[train_model.M] = masks
policy_loss, value_loss, policy_entropy, _ = sess.run(
[pg_loss, vf_loss, entropy, _train], td_map)
return policy_loss, value_loss, policy_entropy
self.train = train
self.train_model = train_model
self.step_model = step_model
self.step = step_model.step
self.value = step_model.value
self.initial_state = step_model.initial_state
self.save = functools.partial(tf_util.save_variables, sess=sess)
self.load = functools.partial(tf_util.load_variables, sess=sess)
tf.global_variables_initializer().run(session=sess)
def __init__(self,
*,
policy,
ob_space,
ac_space,
nbatch_act,
nbatch_train,
nsteps,
ent_coef,
vf_coef,
max_grad_norm,
mpi_rank_weight=1,
comm=None,
microbatch_size=None,
model_index=0):
self.sess = sess = get_session()
self.model_index = model_index
if MPI is not None and comm is None:
comm = MPI.COMM_WORLD
with tf.variable_scope('ppo2_model%s' % model_index,
reuse=tf.AUTO_REUSE):
# CREATE OUR TWO MODELS
# act_model that is used for sampling
act_model = policy(nbatch_act, 1, sess)
# Train model for training
if microbatch_size is None:
train_model = policy(nbatch_train, nsteps, sess)
else:
train_model = policy(microbatch_size, nsteps, sess)
# CREATE THE PLACEHOLDERS
self.A = A = train_model.pdtype.sample_placeholder([None])
self.ADV = ADV = tf.placeholder(tf.float32, [None])
self.R = R = tf.placeholder(tf.float32, [None])
# Keep track of old actor
self.OLDNEGLOGPAC = OLDNEGLOGPAC = tf.placeholder(tf.float32, [None])
# Keep track of old critic
self.OLDVPRED = OLDVPRED = tf.placeholder(tf.float32, [None])
self.LR = LR = tf.placeholder(tf.float32, [])
# Cliprange
self.CLIPRANGE = CLIPRANGE = tf.placeholder(tf.float32, [])
neglogpac = train_model.pd.neglogp(A)
# Calculate the entropy
# Entropy is used to improve exploration by limiting the premature convergence to suboptimal policy.
entropy = tf.reduce_mean(train_model.pd.entropy())
# CALCULATE THE LOSS
# Total loss = Policy gradient loss - entropy * entropy coefficient + Value coefficient * value loss
# Clip the value to reduce variability during Critic training
# Get the predicted value
vpred = train_model.vf
vpredclipped = OLDVPRED + tf.clip_by_value(train_model.vf - OLDVPRED,
-CLIPRANGE, CLIPRANGE)
# Unclipped value
vf_losses1 = tf.square(vpred - R)
# Clipped value
vf_losses2 = tf.square(vpredclipped - R)
vf_loss = .5 * tf.reduce_mean(tf.maximum(vf_losses1, vf_losses2))
# Calculate ratio (pi current policy / pi old policy)
ratio = tf.exp(OLDNEGLOGPAC - neglogpac)
# Defining Loss = - J is equivalent to max J
pg_losses = -ADV * ratio
pg_losses2 = -ADV * tf.clip_by_value(ratio, 1.0 - CLIPRANGE,
1.0 + CLIPRANGE)
# Final PG loss
pg_loss = tf.reduce_mean(tf.maximum(pg_losses, pg_losses2))
approxkl = .5 * tf.reduce_mean(tf.square(neglogpac - OLDNEGLOGPAC))
clipfrac = tf.reduce_mean(
tf.to_float(tf.greater(tf.abs(ratio - 1.0), CLIPRANGE)))
# Total loss
loss = pg_loss - entropy * ent_coef + vf_loss * vf_coef
# UPDATE THE PARAMETERS USING LOSS
# 1. Get the model parameters
params = tf.trainable_variables('ppo2_model%s' % model_index)
# print("para",model_index,params)
# 2. Build our trainer
if comm is not None and comm.Get_size() > 1:
self.trainer = MpiAdamOptimizer(comm,
learning_rate=LR,
mpi_rank_weight=mpi_rank_weight,
epsilon=1e-5)
else:
self.trainer = tf.train.AdamOptimizer(learning_rate=LR,
epsilon=1e-5)
# 3. Calculate the gradients
grads_and_var = self.trainer.compute_gradients(loss, params)
grads, var = zip(*grads_and_var)
if max_grad_norm is not None:
# Clip the gradients (normalize)
grads, _grad_norm = tf.clip_by_global_norm(grads, max_grad_norm)
grads_and_var = list(zip(grads, var))
# zip aggregate each gradient with parameters associated
# For instance zip(ABCD, xyza) => Ax, By, Cz, Da
self.grads = grads
self.var = var
self._train_op = self.trainer.apply_gradients(grads_and_var)
self.loss_names = [
'policy_loss', 'value_loss', 'policy_entropy', 'approxkl',
'clipfrac'
]
self.stats_list = [pg_loss, vf_loss, entropy, approxkl, clipfrac]
self.train_model = train_model
self.act_model = act_model
self.step = act_model.step
self.value = act_model.value
self.initial_state = act_model.initial_state
self.save = functools.partial(save_variables, sess=sess)
self.load = functools.partial(load_variables, sess=sess)
initialize()
global_variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope="")
# print("global_variables",model_index,global_variables)
if MPI is not None:
sync_from_root(sess, global_variables, comm=comm) #pylint: disable=E1101