def setup_model(self):
with SetVerbosity(self.verbose):
self.graph = tf.Graph()
with self.graph.as_default():
self.sess = tf_util.make_session(num_cpu=self.nprocs,
graph=self.graph)
self.advs_ph = advs_ph = tf.placeholder(tf.float32, [None])
self.rewards_ph = rewards_ph = tf.placeholder(
tf.float32, [None])
self.pg_lr_ph = pg_lr_ph = tf.placeholder(tf.float32, [])
n_batch_step = None
n_batch_train = None
if issubclass(self.policy, LstmPolicy):
n_batch_step = self.n_envs
n_batch_train = self.n_envs * self.n_steps
self.model = step_model = self.policy(self.sess,
self.observation_space,
self.action_space,
self.n_envs,
1,
n_batch_step,
reuse=False)
self.model2 = train_model = self.policy(self.sess,
self.observation_space,
self.action_space,
self.n_envs,
self.n_steps,
n_batch_train,
reuse=True)
self.action_ph = action_ph = train_model.pdtype.sample_placeholder(
[None])
logpac = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=train_model.policy, labels=action_ph)
self.logits = train_model.policy
# training loss
pg_loss = tf.reduce_mean(advs_ph * logpac)
self.entropy = entropy = tf.reduce_mean(
calc_entropy(train_model.policy))
self.pg_loss = pg_loss = pg_loss - self.ent_coef * entropy
self.vf_loss = vf_loss = mse(tf.squeeze(train_model.value_fn),
rewards_ph)
train_loss = pg_loss + self.vf_coef * vf_loss
# Fisher loss construction
self.pg_fisher = pg_fisher_loss = -tf.reduce_mean(logpac)
sample_net = train_model.value_fn + tf.random_normal(
tf.shape(train_model.value_fn))
self.vf_fisher = vf_fisher_loss = -self.vf_fisher_coef * tf.reduce_mean(
tf.pow(train_model.value_fn - tf.stop_gradient(sample_net),
2))
self.joint_fisher = pg_fisher_loss + vf_fisher_loss
self.params = params = find_trainable_variables("model")
self.grads_check = tf.gradients(train_loss, params)
with tf.device('/gpu:0'):
self.optim = optim = kfac.KfacOptimizer(
learning_rate=pg_lr_ph,
clip_kl=self.kfac_clip,
momentum=0.9,
kfac_update=1,
epsilon=0.01,
stats_decay=0.99,
async=1,
cold_iter=10,
max_grad_norm=self.max_grad_norm,
verbose=self.verbose)
optim.compute_and_apply_stats(self.joint_fisher,
var_list=params)
self.train_model = train_model
self.step_model = step_model
self.step = step_model.step
self.proba_step = step_model.proba_step
self.value = step_model.value
self.initial_state = step_model.initial_state
tf.global_variables_initializer().run(session=self.sess)
def setup_model(self):
with SetVerbosity(self.verbose):
assert issubclass(self.policy, ActorCriticPolicy), "Error: the input policy for the ACKTR model must be " \
"an instance of common.policies.ActorCriticPolicy."
if isinstance(self.action_space, Box):
raise NotImplementedError(
"WIP: ACKTR does not support Continuous actions yet.")
self.graph = tf.Graph()
with self.graph.as_default():
self.sess = tf_util.make_session(num_cpu=self.nprocs,
graph=self.graph)
n_batch_step = None
n_batch_train = None
if issubclass(self.policy, RecurrentActorCriticPolicy):
n_batch_step = self.n_envs
n_batch_train = self.n_envs * self.n_steps
self.model = step_model = self.policy(self.sess,
self.observation_space,
self.action_space,
self.n_envs,
1,
n_batch_step,
reuse=False,
**self.policy_kwargs)
self.params = params = tf_util.get_trainable_vars("model")
with tf.variable_scope(
"train_model",
reuse=True,
custom_getter=tf_util.outer_scope_getter(
"train_model")):
self.model2 = train_model = self.policy(
self.sess,
self.observation_space,
self.action_space,
self.n_envs,
self.n_steps,
n_batch_train,
reuse=True,
**self.policy_kwargs)
with tf.variable_scope(
"loss",
reuse=False,
custom_getter=tf_util.outer_scope_getter("loss")):
self.advs_ph = advs_ph = tf.placeholder(tf.float32, [None])
self.rewards_ph = rewards_ph = tf.placeholder(
tf.float32, [None])
self.pg_lr_ph = pg_lr_ph = tf.placeholder(tf.float32, [])
self.action_ph = action_ph = train_model.pdtype.sample_placeholder(
[None])
logpac = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=train_model.policy, labels=action_ph)
self.logits = train_model.policy
# training loss
pg_loss = tf.reduce_mean(advs_ph * logpac)
self.entropy = entropy = tf.reduce_mean(
calc_entropy(train_model.policy))
self.pg_loss = pg_loss = pg_loss - self.ent_coef * entropy
self.vf_loss = vf_loss = mse(
tf.squeeze(train_model.value_fn), rewards_ph)
train_loss = pg_loss + self.vf_coef * vf_loss
# Fisher loss construction
self.pg_fisher = pg_fisher_loss = -tf.reduce_mean(logpac)
sample_net = train_model.value_fn + tf.random_normal(
tf.shape(train_model.value_fn))
self.vf_fisher = vf_fisher_loss = -self.vf_fisher_coef * tf.reduce_mean(
tf.pow(
train_model.value_fn -
tf.stop_gradient(sample_net), 2))
self.joint_fisher = pg_fisher_loss + vf_fisher_loss
tf.summary.scalar('entropy_loss', self.entropy)
tf.summary.scalar('policy_gradient_loss', pg_loss)
tf.summary.scalar('policy_gradient_fisher_loss',
pg_fisher_loss)
tf.summary.scalar('value_function_loss', self.vf_loss)
tf.summary.scalar('value_function_fisher_loss',
vf_fisher_loss)
tf.summary.scalar('loss', train_loss)
self.grads_check = tf.gradients(train_loss, params)
with tf.variable_scope("input_info", reuse=False):
tf.summary.scalar('discounted_rewards',
tf.reduce_mean(self.rewards_ph))
tf.summary.scalar('learning_rate',
tf.reduce_mean(self.pg_lr_ph))
tf.summary.scalar('advantage',
tf.reduce_mean(self.advs_ph))
if self.full_tensorboard_log:
tf.summary.histogram('discounted_rewards',
self.rewards_ph)
tf.summary.histogram('learning_rate', self.pg_lr_ph)
tf.summary.histogram('advantage', self.advs_ph)
if tf_util.is_image(self.observation_space):
tf.summary.image('observation', train_model.obs_ph)
else:
tf.summary.histogram('observation',
train_model.obs_ph)
with tf.variable_scope(
"kfac",
reuse=False,
custom_getter=tf_util.outer_scope_getter("kfac")):
with tf.device('/gpu:0'):
self.optim = optim = kfac.KfacOptimizer(
learning_rate=pg_lr_ph,
clip_kl=self.kfac_clip,
momentum=0.9,
kfac_update=1,
epsilon=0.01,
stats_decay=0.99,
async_eigen_decomp=self.async_eigen_decomp,
cold_iter=10,
max_grad_norm=self.max_grad_norm,
verbose=self.verbose)
optim.compute_and_apply_stats(self.joint_fisher,
var_list=params)
self.train_model = train_model
self.step_model = step_model
self.step = step_model.step
self.proba_step = step_model.proba_step
self.value = step_model.value
self.initial_state = step_model.initial_state
tf.global_variables_initializer().run(session=self.sess)
self.summary = tf.summary.merge_all()