def make_vars(self, stepnum='0'):
# lists over the meta_batch_size
obs_vars, action_vars, adv_vars, imp_vars = [], [], [], []
for i in range(self.meta_batch_size):
obs_vars.append(
self.env.observation_space.new_tensor_variable(
'obs' + stepnum + '_' + str(i),
extra_dims=1,
))
action_vars.append(
self.env.action_space.new_tensor_variable(
'action' + stepnum + '_' + str(i),
extra_dims=1,
))
adv_vars.append(
tensor_utils.new_tensor(
name='advantage' + stepnum + '_' + str(i),
ndim=1,
dtype=tf.float32,
))
imp_vars.append(
tensor_utils.new_tensor(
name='imp_ratios' + stepnum + '_' + str(i),
ndim=1,
dtype=tf.float32,
))
return obs_vars, action_vars, adv_vars, imp_vars
def make_vars(self):
obs_var = self.env.observation_space.new_tensor_variable(
'obs',
extra_dims=1,
)
action_var = self.env.action_space.new_tensor_variable(
'action',
extra_dims=1,
)
adv_var = tensor_utils.new_tensor(
name='advantage',
ndim=1,
dtype=tf.float32,
)
noise_var = tf.placeholder(dtype=tf.float32,
shape=[None, self.latent_dim],
name='noise')
task_idx_var = tensor_utils.new_tensor(
name='task_idx',
ndim=1,
dtype=tf.int32,
)
return obs_var, action_var, adv_var, noise_var, task_idx_var
def make_vars(self, stepnum='0'):
# lists over the meta_batch_size
obs_vars, action_vars, adv_vars, noise_vars, task_idx_vars = [], [], [], [], []
for i in range(self.meta_batch_size):
obs_vars.append(
self.env.observation_space.new_tensor_variable(
'obs' + stepnum + '_' + str(i),
extra_dims=1,
))
action_vars.append(
self.env.action_space.new_tensor_variable(
'action' + stepnum + '_' + str(i),
extra_dims=1,
))
adv_vars.append(
tensor_utils.new_tensor(
name='advantage' + stepnum + '_' + str(i),
ndim=1,
dtype=tf.float32,
))
noise_vars.append(
tf.placeholder(dtype=tf.float32,
shape=[None, self.latent_dim],
name='noise' + stepnum + '_' + str(i)))
task_idx_vars.append(
tensor_utils.new_tensor(
name='task_idx' + stepnum + '_' + str(i),
ndim=1,
dtype=tf.int32,
))
return obs_vars, action_vars, adv_vars, noise_vars, task_idx_vars
def _init_graph(self, chunk_size):
with self._graph.as_default():
with tf.variable_scope('SimilarityCalculator'):
X = tensor_utils.new_tensor(
'X',
ndim=2,
dtype=tf.float32,
)
pool = tensor_utils.new_tensor(
'pool',
ndim=2,
dtype=tf.float32,
)
division_factor = tensor_utils.new_tensor(
'division_factor',
ndim=0,
dtype=tf.float32,
)
inputs = [X, pool, division_factor]
size = tf.shape(X)[0]
if chunk_size is None:
chunk_size = size
chunk_size_float = tf.cast(chunk_size, tf.float32)
else:
chunk_size_float = float(chunk_size)
array_size = tf.cast(
tf.ceil(tf.cast(size, tf.float32) / chunk_size_float),
tf.int32)
ta_initial = tf.TensorArray(dtype=tf.float32,
size=array_size,
infer_shape=False)
def _cond(idx, i, ta):
return i < size
def _body(idx, i, ta):
until = tf.minimum(i + chunk_size, size)
new_pdiffs = (X[i:until, tf.newaxis, :] - pool)
squared_l2 = tf.reduce_sum(tf.square(new_pdiffs), axis=-1)
part_similarities = tf.reduce_mean(tf.exp(-squared_l2 /
division_factor),
axis=1)
return idx + 1, until, ta.write(idx, part_similarities)
final_idx, final_i, ta = tf.while_loop(
_cond,
_body,
loop_vars=[0, 0, ta_initial],
parallel_iterations=1)
result = ta.concat()
self._get_result = tensor_utils.compile_function(
inputs=inputs,
outputs=result,
)
def make_vars(self, stepnum='0'):
# lists over the meta_batch_size
# We should only need the last stepnum for meta-optimization.
obs_vars, action_vars, adv_vars, rewards_vars, returns_vars, path_lengths_vars, expert_action_vars = [], [], [], [], [], [], []
for i in range(self.meta_batch_size):
obs_vars.append(
self.env.observation_space.new_tensor_variable(
'obs' + stepnum + '_' + str(i),
extra_dims=1,
add_to_flat_dim=(0 if self.extra_input is None else
self.extra_input_dim),
))
action_vars.append(
self.env.action_space.new_tensor_variable(
'action' + stepnum + '_' + str(i),
extra_dims=1,
))
adv_vars.append(
tensor_utils.new_tensor(
'advantage' + stepnum + '_' + str(i),
ndim=1,
dtype=tf.float32,
))
if self.metalearn_baseline:
rewards_vars.append(
tensor_utils.new_tensor(
'rewards' + stepnum + '_' + str(i),
ndim=1,
dtype=tf.float32,
))
returns_vars.append(
tensor_utils.new_tensor(
'returns' + stepnum + '_' + str(i),
ndim=1,
dtype=tf.float32,
))
# path_lengths_vars.append(tensor_utils.new_tensor(
# 'path_lengths' + stepnum + '_' + str(i),
# ndim=1, dtype=tf.float32,
# ))
expert_action_vars.append(
tensor_utils.new_tensor(
'expert_actions' + stepnum + '_' + str(i),
ndim=1,
dtype=tf.float32,
))
if not self.metalearn_baseline:
return obs_vars, action_vars, adv_vars, expert_action_vars
else:
return obs_vars, action_vars, adv_vars, rewards_vars, returns_vars, expert_action_vars # path_lengths_vars before expert action
def make_vars(self, stepnum='0'):
# lists over the meta_batch_size
obs_vars, action_vars, adv_vars = [], [], []
for i in range(self.meta_batch_size):
obs_vars.append(
self.env.observation_space.new_tensor_variable(
'obs' + stepnum + '_' + str(i),
extra_dims=1,
))
action_vars.append(
tf.placeholder(tf.float32,
shape=[None] +
[self.env.action_space.flat_dim * 20],
name='action' + stepnum + '_' + str(i)))
#action_vars.append(self.env.action_space.new_tensor_variable(
# 'action' + stepnum + '_' + str(i),
# extra_dims=1,
#))
adv_vars.append(
tensor_utils.new_tensor(
name='advantage' + stepnum + '_' + str(i),
ndim=1,
dtype=tf.float32,
))
return obs_vars, action_vars, adv_vars
def init_opt(self):
is_recurrent = int(self.policy.recurrent)
obs_var = self.env.observation_space.new_tensor_variable(
'obs',
extra_dims=1 + is_recurrent,
)
action_var = self.env.action_space.new_tensor_variable(
'action',
extra_dims=1 + is_recurrent,
)
advantage_var = tensor_utils.new_tensor(
'advantage',
ndim=1 + is_recurrent,
dtype=tf.float32,
)
dist = self.policy.distribution
old_dist_info_vars = {
k: tf.placeholder(tf.float32, shape=[None] * (1 + is_recurrent) + list(shape), name='old_%s' % k)
for k, shape in dist.dist_info_specs
}
old_dist_info_vars_list = [old_dist_info_vars[k] for k in dist.dist_info_keys]
state_info_vars = {
k: tf.placeholder(tf.float32, shape=[None] * (1 + is_recurrent) + list(shape), name=k)
for k, shape in self.policy.state_info_specs
}
state_info_vars_list = [state_info_vars[k] for k in self.policy.state_info_keys]
if is_recurrent:
valid_var = tf.placeholder(tf.float32, shape=[None, None], name="valid")
else:
valid_var = None
dist_info_vars = self.policy.dist_info_sym(obs_var, state_info_vars)
kl = dist.kl_sym(old_dist_info_vars, dist_info_vars)
lr = dist.likelihood_ratio_sym(action_var, old_dist_info_vars, dist_info_vars)
if is_recurrent:
mean_kl = tf.reduce_sum(kl * valid_var) / tf.reduce_sum(valid_var)
surr_loss = - tf.reduce_sum(lr * advantage_var * valid_var) / tf.reduce_sum(valid_var)
else:
mean_kl = tf.reduce_mean(kl)
surr_loss = - tf.reduce_mean(lr * advantage_var)
input_list = [
obs_var,
action_var,
advantage_var,
] + state_info_vars_list + old_dist_info_vars_list
if is_recurrent:
input_list.append(valid_var)
self.optimizer.update_opt(
loss=surr_loss,
target=self.policy,
leq_constraint=(mean_kl, self.step_size),
inputs=input_list,
constraint_name="mean_kl"
)
return dict()
def init_opt(self):
is_recurrent = int(self.policy.recurrent)
obs_var = self.env.observation_space.new_tensor_variable(
'obs',
extra_dims=1 + is_recurrent,
)
action_var = self.env.action_space.new_tensor_variable(
'action',
extra_dims=1 + is_recurrent,
)
advantage_var = tensor_utils.new_tensor(
'advantage',
ndim=1 + is_recurrent,
dtype=tf.float32,
)
dist = self.policy.distribution
old_dist_info_vars = {
k: tf.placeholder(tf.float32, shape=[None] * (1 + is_recurrent) + list(shape), name='old_%s' % k)
for k, shape in dist.dist_info_specs
}
old_dist_info_vars_list = [old_dist_info_vars[k] for k in dist.dist_info_keys]
state_info_vars = {
k: tf.placeholder(tf.float32, shape=[None] * (1 + is_recurrent) + list(shape), name=k)
for k, shape in self.policy.state_info_specs
}
state_info_vars_list = [state_info_vars[k] for k in self.policy.state_info_keys]
if is_recurrent:
valid_var = tf.placeholder(tf.float32, shape=[None, None], name="valid")
else:
valid_var = None
dist_info_vars = self.policy.dist_info_sym(obs_var, state_info_vars)
kl = dist.kl_sym(old_dist_info_vars, dist_info_vars)
lr = dist.likelihood_ratio_sym(action_var, old_dist_info_vars, dist_info_vars)
if is_recurrent:
mean_kl = tf.reduce_sum(kl * valid_var) / tf.reduce_sum(valid_var)
surr_loss = - tf.reduce_sum(lr * advantage_var * valid_var) / tf.reduce_sum(valid_var)
else:
mean_kl = tf.reduce_mean(kl)
surr_loss = - tf.reduce_mean(lr * advantage_var)
input_list = [
obs_var,
action_var,
advantage_var,
] + state_info_vars_list + old_dist_info_vars_list
if is_recurrent:
input_list.append(valid_var)
self.optimizer.update_opt(
loss=surr_loss,
target=self.policy,
leq_constraint=(mean_kl, self.step_size),
inputs=input_list,
constraint_name="mean_kl"
)
return dict()
def opt_helper(self, policy, optimizer):
is_recurrent = int(policy.recurrent)
obs_var = self.env.observation_space.new_tensor_variable(
'obs',
extra_dims=1 + is_recurrent,)
action_var = self.env.action_space.new_tensor_variable(
'action',
extra_dims=1 + is_recurrent,)
advantage_var = tensor_utils.new_tensor(
name='advantage',
ndim=1 + is_recurrent,
dtype=tf.float32,)
dist = policy.distribution
old_dist_info_vars = {
k: tf.placeholder(tf.float32, shape=[None] * (1 + is_recurrent) + list(shape),
name='old_%s' % k)
for k, shape in dist.dist_info_specs
}
old_dist_info_vars_list = [old_dist_info_vars[k] for k in dist.dist_info_keys]
state_info_vars = {
k: tf.placeholder(tf.float32, shape=[None] * (1 + is_recurrent) + list(shape), name=k)
for k, shape in policy.state_info_specs
}
state_info_vars_list = [state_info_vars[k] for k in policy.state_info_keys]
if is_recurrent:
valid_var = tf.placeholder(tf.float32, shape=[None, None], name="valid")
else:
valid_var = None
dist_info_vars = policy.dist_info_sym(obs_var, state_info_vars)
logli = dist.log_likelihood_sym(action_var, dist_info_vars)
kl = dist.kl_sym(old_dist_info_vars, dist_info_vars)
# formulate as a minimization problem
# The gradient of the surrogate objective is the policy gradient
if is_recurrent:
surr_obj = -tf.reduce_sum(logli * advantage_var * valid_var) / tf.reduce_sum(valid_var)
mean_kl = tf.reduce_sum(kl * valid_var) / tf.reduce_sum(valid_var)
max_kl = tf.reduce_max(kl * valid_var)
else:
surr_obj = -tf.reduce_mean(logli * advantage_var)
mean_kl = tf.reduce_mean(kl)
max_kl = tf.reduce_max(kl)
input_list = [obs_var, action_var, advantage_var] + state_info_vars_list
if is_recurrent:
input_list.append(valid_var)
optimizer.update_opt(loss=surr_obj, target=policy, inputs=input_list)
f_kl = tensor_utils.compile_function(
inputs=input_list + old_dist_info_vars_list,
outputs=[mean_kl, max_kl],)
opt_info = dict(f_kl=f_kl,)
return opt_info
def init_opt(self):
obs_var = self.env.observation_space.new_tensor_variable(
'obs',
extra_dims=1,
)
action_var = self.env.action_space.new_tensor_variable(
'action',
extra_dims=1,
)
advantage_var = tensor_utils.new_tensor(
name='advantage',
ndim=1,
dtype=tf.float32,
)
dist = self.policy.distribution
old_dist_info_vars = {
k: tf.placeholder(tf.float32,
shape=[None] + list(shape),
name='old_%s' % k)
for k, shape in dist.dist_info_specs
}
old_dist_info_vars_list = [
old_dist_info_vars[k] for k in dist.dist_info_keys
]
state_info_vars = {
k: tf.placeholder(tf.float32, shape=[None] + list(shape), name=k)
for k, shape in self.policy.state_info_specs
}
state_info_vars_list = [
state_info_vars[k] for k in self.policy.state_info_keys
]
dist_info_vars = self.policy.dist_info_sym(obs_var, state_info_vars)
# todo, delete this var
loglik = dist.log_likelihood_sym(action_var, dist_info_vars)
kl = dist.kl_sym(old_dist_info_vars, dist_info_vars)
# formulate as a minimization problem
# The gradient of the surrogate objective is the policy gradient
surr_obj = -tf.reduce_mean(loglik * advantage_var)
mean_kl = tf.reduce_mean(kl)
max_kl = tf.reduce_max(kl)
input_list = [obs_var, action_var, advantage_var
] + state_info_vars_list + old_dist_info_vars_list
self.optimizer.update_opt(loss=surr_obj,
target=self.policy,
leq_constraint=(mean_kl, self.delta),
inputs=input_list)
f_kl = tensor_utils.compile_function(
inputs=input_list + old_dist_info_vars_list,
outputs=[mean_kl, max_kl],
)
self.opt_info = dict(f_kl=f_kl, )
def make_vars_latent(self):
# lists over the meta_batch_size
adv_var = tensor_utils.new_tensor(
name='advantage_latent',
ndim=1,
dtype=tf.float32,
)
z_var = tf.placeholder(dtype=tf.float32,
shape=[None, self.latent_dim],
name='zs_latent')
task_idx_var = tensor_utils.new_tensor(
name='task_idx_latent',
ndim=1,
dtype=tf.int32,
)
return adv_var, z_var, task_idx_var
def __init__(self, env_spec, reg_coeff=1e-5):
self._coeffs = None
self._reg_coeff = reg_coeff
self.feature_mat = tensor_utils.new_tensor(
'feature_mat',
ndim=2,
dtype=tf.float32,
)
self.returns = tensor_utils.new_tensor(
'returns',
ndim=2,
dtype=tf.float32,
)
# import pdb; pdb.set_trace()
ident = tf.identity(self.feature_mat)
self.train_ops = tf.matrix_solve_ls(tf.square(self.feature_mat) +
self._reg_coeff * ident,
self.returns,
fast=False)
self.sess = tf.Session()
def make_vars_latent(self, stepnum='0'):
# lists over the meta_batch_size
adv_vars, z_vars, task_idx_vars = [], [], []
for i in range(self.meta_batch_size):
adv_vars.append(
tensor_utils.new_tensor(
name='advantage_latent' + stepnum + '_' + str(i),
ndim=1,
dtype=tf.float32,
))
z_vars.append(
tf.placeholder(dtype=tf.float32,
shape=[None, self.latent_dim],
name='zs_latent' + stepnum + '_' + str(i)))
task_idx_vars.append(
tensor_utils.new_tensor(
name='task_idx_latents' + stepnum + '_' + str(i),
ndim=1,
dtype=tf.int32,
))
return adv_vars, z_vars, task_idx_vars
def init_opt(self):
is_recurrent = int(self.policy.recurrent)
obs_var = self.env.observation_space.new_tensor_variable(
'obs',
extra_dims=1 + is_recurrent,
)
action_var = self.env.action_space.new_tensor_variable(
'action',
extra_dims=1 + is_recurrent,
)
advantage_var = tensor_utils.new_tensor(
name='advantage',
ndim=1 + is_recurrent,
dtype=tf.float32,
)
dist = self.policy.distribution
old_dist_info_vars = {
k: tf.placeholder(tf.float32, shape=[None] * (1 + is_recurrent) + list(shape), name='old_%s' % k)
for k, shape in dist.dist_info_specs
}
old_dist_info_vars_list = [old_dist_info_vars[k] for k in dist.dist_info_keys]
state_info_vars = {
k: tf.placeholder(tf.float32, shape=[None] * (1 + is_recurrent) + list(shape), name=k)
for k, shape in self.policy.state_info_specs
}
state_info_vars_list = [state_info_vars[k] for k in self.policy.state_info_keys]
self.input_list_for_grad = [obs_var, action_var, advantage_var] + state_info_vars_list
if is_recurrent:
valid_var = tf.placeholder(tf.float32, shape=[None, None], name="valid")
else:
valid_var = None
dist_info_vars = self.policy.dist_info_sym(obs_var, state_info_vars)
logli = dist.log_likelihood_sym(action_var, dist_info_vars)
kl = dist.kl_sym(old_dist_info_vars, dist_info_vars)
# formulate as a minimization problem
# The gradient of the surrogate objective is the policy gradient
if is_recurrent:
surr_obj = - tf.reduce_sum(logli * advantage_var * valid_var) / tf.reduce_sum(valid_var)
mean_kl = tf.reduce_sum(kl * valid_var) / tf.reduce_sum(valid_var)
max_kl = tf.reduce_max(kl * valid_var)
else:
surr_obj = - tf.reduce_mean(logli * advantage_var)
mean_kl = tf.reduce_mean(kl)
max_kl = tf.reduce_max(kl)
self.surr_obj = surr_obj
def make_vars(self, stepnum='0'):
# lists over the meta_batch_size
obs_vars, action_vars, adv_vars = [], [], []
for i in range(self.meta_batch_size):
obs_vars.append(self.env.observation_space.new_tensor_variable(
'obs' + stepnum + '_' + str(i),
extra_dims=1,
))
action_vars.append(self.env.action_space.new_tensor_variable(
'action' + stepnum + '_' + str(i),
extra_dims=1,
))
adv_vars.append(tensor_utils.new_tensor(
name='advantage' + stepnum + '_' + str(i),
ndim=1, dtype=tf.float32,
))
return obs_vars, action_vars, adv_vars
def init_opt(self):
is_recurrent = int(self.policy.recurrent)
obs_var = self.env.observation_space.new_tensor_variable(
'obs',
extra_dims=1 + is_recurrent,
)
nobs_var = self.env.observation_space.new_tensor_variable(
'nobs',
extra_dims=1 + is_recurrent,
)
action_var = self.env.action_space.new_tensor_variable(
'action',
extra_dims=1 + is_recurrent,
)
advantage_var = tensor_utils.new_tensor(
'advantage',
ndim=1 + is_recurrent,
dtype=tf.float32,
)
empw_var = tensor_utils.new_tensor(
'empowerment',
ndim=2 + is_recurrent,
dtype=tf.float32,
)
input_list = [
obs_var,
nobs_var,
action_var,
advantage_var,
empw_var,
]
dist = self.policy.distribution
old_dist_info_vars = {
k: tf.placeholder(tf.float32,
shape=[None] * (1 + is_recurrent) + list(shape),
name='old_%s' % k)
for k, shape in dist.dist_info_specs
}
old_dist_info_vars_list = [
old_dist_info_vars[k] for k in dist.dist_info_keys
]
state_info_vars = {
k: tf.placeholder(tf.float32,
shape=[None] * (1 + is_recurrent) + list(shape),
name=k)
for k, shape in self.policy.state_info_specs
}
state_info_vars_list = [
state_info_vars[k] for k in self.policy.state_info_keys
]
if is_recurrent:
valid_var = tf.placeholder(tf.float32,
shape=[None, None],
name="valid")
else:
valid_var = None
dist_info_vars = self.policy.dist_info_sym(obs_var, state_info_vars)
# dist_info_vars["mean"]=dist_info_vars["mean"]+empw_var
q_input = tf.concat([obs_var, nobs_var], axis=1)
q_dist_info_vars = self.qvar_model.dist_info_sym(
q_input, state_info_vars)
kl = dist.kl_sym(old_dist_info_vars, dist_info_vars)
lr = dist.likelihood_ratio_sym(action_var, old_dist_info_vars,
dist_info_vars)
if self.pol_ent_wt > 0:
if 'log_std' in dist_info_vars:
log_std = dist_info_vars['log_std']
ent = tf.reduce_sum(log_std +
tf.log(tf.sqrt(2 * np.pi * np.e)),
reduction_indices=-1)
elif 'prob' in dist_info_vars:
prob = dist_info_vars['prob']
ent = -tf.reduce_sum(prob * tf.log(prob), reduction_indices=-1)
else:
raise NotImplementedError()
ent = tf.stop_gradient(ent)
adv = advantage_var + self.pol_ent_wt * ent
else:
adv = advantage_var
if is_recurrent:
mean_kl = tf.reduce_sum(kl * valid_var) / tf.reduce_sum(valid_var)
surr_loss = -tf.reduce_sum(
lr * adv * valid_var) / tf.reduce_sum(valid_var)
else:
mean_kl = tf.reduce_mean(kl)
surr_loss = -tf.reduce_mean(lr * adv)
if self.train_empw:
print(
"training empowerment========================================")
pred = dist.log_likelihood(dist.sample(dist_info_vars),
dist_info_vars) + empw_var
target = dist.log_likelihood(dist.sample(q_dist_info_vars),
q_dist_info_vars)
# print("pred = {}, target={}".format(pred.shape, target.shape))
surr_loss = surr_loss + self.lambda_i * tf.losses.mean_squared_error(
predictions=pred, labels=target)
input_list += state_info_vars_list + old_dist_info_vars_list
if is_recurrent:
input_list.append(valid_var)
self.optimizer.update_opt(loss=surr_loss,
target=self.policy,
leq_constraint=(mean_kl, self.step_size),
inputs=input_list,
constraint_name="mean_kl")
return dict()
def init_opt(self):
is_recurrent = int(self.policy.recurrent)
obs_var = self.env.observation_space.new_tensor_variable(
'obs',
extra_dims=1 + is_recurrent,
add_to_flat_dim=(0 if self.extra_input is None else
self.extra_input_dim),
)
action_var = self.env.action_space.new_tensor_variable(
'action',
extra_dims=1 + is_recurrent,
)
advantage_var = tensor_utils.new_tensor(
name='advantage',
ndim=1 + is_recurrent,
dtype=tf.float32,
)
dist = self.policy.distribution
old_dist_info_vars = {
k: tf.placeholder(tf.float32,
shape=[None] * (1 + is_recurrent) + list(shape),
name='old_%s' % k)
for k, shape in dist.dist_info_specs
}
old_dist_info_vars_list = [
old_dist_info_vars[k] for k in dist.dist_info_keys
]
state_info_vars = {
k: tf.placeholder(tf.float32,
shape=[None] * (1 + is_recurrent) + list(shape),
name=k)
for k, shape in self.policy.state_info_specs
}
state_info_vars_list = [
state_info_vars[k] for k in self.policy.state_info_keys
]
if is_recurrent:
valid_var = tf.placeholder(tf.float32,
shape=[None, None],
name="valid")
else:
valid_var = None
dist_info_vars = self.policy.dist_info_sym(obs_var, state_info_vars)
logli = dist.log_likelihood_sym(action_var, dist_info_vars)
# logli_old = dist.log_likelihood_sym(action_var, old_dist_info_vars)
r__ = dist.likelihood_ratio_sym(action_var, old_dist_info_vars,
dist_info_vars)
clip_frac = tf.reduce_mean(
tf.to_float(tf.greater(tf.abs(r__ - 1.0), 0.2)))
r_ = tf.clip_by_value(r__, 0.8, 1.2)
kl = dist.kl_sym(old_dist_info_vars, dist_info_vars)
# formulate as a minimization problem
# The gradient of the surrogate objective is the policy gradient
if is_recurrent:
surr_obj = -tf.reduce_sum(
r_ * advantage_var * valid_var) / tf.reduce_sum(valid_var)
mean_kl = tf.reduce_sum(kl * valid_var) / tf.reduce_sum(valid_var)
max_kl = tf.reduce_max(kl * valid_var)
else:
surr_obj = -tf.reduce_mean(r_ * advantage_var)
mean_kl = tf.reduce_mean(kl)
max_kl = tf.reduce_max(kl)
input_list = [obs_var, action_var, advantage_var
] + state_info_vars_list + old_dist_info_vars_list
if is_recurrent:
input_list.append(valid_var)
#self.policy.set_init_surr_obj(input_list, [surr_obj]) # debugging
self.optimizer.update_opt(loss=surr_obj,
target=self.policy,
inputs=input_list)
f_kl = tensor_utils.compile_function(
inputs=input_list + old_dist_info_vars_list,
outputs=[mean_kl, max_kl, clip_frac, dist_info_vars['log_std']],
)
self.opt_info = dict(f_kl=f_kl, )
def init_opt(self, name=''):
is_recurrent = int(self.policy.recurrent)
obs_var = self.env.observation_space.new_tensor_variable(
name + 'obs',
extra_dims=1 + is_recurrent,
)
action_var = self.env.action_space.new_tensor_variable(
name + 'action',
extra_dims=1 + is_recurrent,
)
advantage_var = tensor_utils.new_tensor(
name + 'advantage',
ndim=1 + is_recurrent,
dtype=tf.float32,
)
dist = self.policy.distribution
old_dist_info_vars = {
k: tf.placeholder(tf.float32,
shape=[None] * (1 + is_recurrent) + list(shape),
name=name + 'old_%s' % k)
for k, shape in dist.dist_info_specs
}
old_dist_info_vars_list = [
old_dist_info_vars[k] for k in dist.dist_info_keys
]
state_info_vars = {
k: tf.placeholder(tf.float32,
shape=[None] * (1 + is_recurrent) + list(shape),
name=name + k)
for k, shape in self.policy.state_info_specs
}
state_info_vars_list = [
state_info_vars[k] for k in self.policy.state_info_keys
]
if is_recurrent:
valid_var = tf.placeholder(tf.float32,
shape=[None, None],
name=name + "valid")
else:
valid_var = None
input_list = [
obs_var,
action_var,
advantage_var,
] + state_info_vars_list + old_dist_info_vars_list
if is_recurrent:
input_list.append(valid_var)
if self.kl_sample_backups > 0:
kl_obs_var = self.env.observation_space.new_tensor_variable(
name + 'kl_obs',
extra_dims=1 + is_recurrent,
)
kl_old_dist_info_vars = {
k:
tf.placeholder(tf.float32,
shape=[None] * (1 + is_recurrent) + list(shape),
name=name + 'kl_old_%s' % k)
for k, shape in dist.dist_info_specs
}
kl_old_dist_info_vars_list = [
kl_old_dist_info_vars[k] for k in dist.dist_info_keys
]
kl_state_info_vars = {
k:
tf.placeholder(tf.float32,
shape=[None] * (1 + is_recurrent) + list(shape),
name=name + 'kl_%s' % k)
for k, shape in self.policy.state_info_specs
}
kl_state_info_vars_list = [
kl_state_info_vars[k] for k in self.policy.state_info_keys
]
kl_dist_info_vars = self.policy.dist_info_sym(
kl_obs_var, kl_state_info_vars)
kl = dist.kl_sym(kl_old_dist_info_vars, kl_dist_info_vars)
input_list += [
kl_obs_var
] + kl_state_info_vars_list + kl_old_dist_info_vars_list
dist_info_vars = self.policy.dist_info_sym(obs_var,
state_info_vars)
else:
dist_info_vars = self.policy.dist_info_sym(obs_var,
state_info_vars)
kl = dist.kl_sym(old_dist_info_vars, dist_info_vars)
lr = dist.likelihood_ratio_sym(action_var, old_dist_info_vars,
dist_info_vars)
if not self.qprop:
if is_recurrent:
mean_kl = tf.reduce_sum(
kl * valid_var) / tf.reduce_sum(valid_var)
surr_loss = -tf.reduce_sum(
lr * advantage_var * valid_var) / tf.reduce_sum(valid_var)
else:
mean_kl = tf.reduce_mean(kl)
surr_loss = -tf.reduce_mean(lr * advantage_var)
else:
if is_recurrent: raise NotImplementedError
eta_var = tensor_utils.new_tensor(
'eta',
ndim=1 + is_recurrent,
dtype=tf.float32,
)
surr_loss = -tf.reduce_mean(lr * advantage_var)
if self.qprop_nu > 0: surr_loss *= 1 - self.qprop_nu
if self.sample_backups > 0 or not self.policy_sample_last:
off_obs_var = self.env.observation_space.new_tensor_variable(
name + 'off_obs',
extra_dims=1 + is_recurrent,
)
off_e_qval = self.qf.get_e_qval_sym(off_obs_var,
self.policy,
deterministic=True)
input_list += [off_obs_var]
surr_loss -= tf.reduce_mean(off_e_qval) # * eta_var)
else:
if not self.mqprop:
# Originally, we subtract this value for the bias correction, but we don't do that if we want mqprop (no action-conditional baseline).
e_qval = self.qf.get_e_qval_sym(obs_var,
self.policy,
deterministic=True)
surr_loss -= tf.reduce_mean(e_qval * eta_var)
mean_kl = tf.reduce_mean(kl)
input_list += [eta_var]
control_variate = self.qf.get_cv_sym(obs_var, action_var,
self.policy)
f_control_variate = tensor_utils.compile_function(
inputs=[obs_var, action_var],
outputs=control_variate,
)
self.opt_info_qprop = dict(f_control_variate=f_control_variate, )
if self.ac_delta > 0:
ac_obs_var = self.env.observation_space.new_tensor_variable(
name + 'ac_obs',
extra_dims=1 + is_recurrent,
)
e_qval = self.qf.get_e_qval_sym(ac_obs_var,
self.policy,
deterministic=True)
input_list += [ac_obs_var]
surr_loss *= (1.0 - self.ac_delta)
surr_loss -= self.ac_delta * tf.reduce_mean(e_qval)
self.optimizer.update_opt(loss=surr_loss,
target=self.policy,
leq_constraint=(mean_kl, self.step_size),
inputs=input_list,
constraint_name="mean_kl")
self.opt_info = dict(target_policy=self.policy, )
self.init_opt_critic()
return dict()
def init_opt(self):
is_recurrent = int(self.policy.recurrent)
obs_var = self.env.observation_space.new_tensor_variable(
'obs',
extra_dims=1 + is_recurrent,
)
action_var = self.env.action_space.new_tensor_variable(
'action',
extra_dims=1 + is_recurrent,
)
advantage_var = tensor_utils.new_tensor(
'advantage',
ndim=1 + is_recurrent,
dtype=tf.float32,
)
dist = self.policy.distribution
old_dist_info_vars = {
k: tf.placeholder(tf.float32,
shape=[None] * (1 + is_recurrent) + list(shape),
name='old_%s' % k)
for k, shape in dist.dist_info_specs
}
old_dist_info_vars_list = [
old_dist_info_vars[k] for k in dist.dist_info_keys
]
state_info_vars = {
k: tf.placeholder(tf.float32,
shape=[None] * (1 + is_recurrent) + list(shape),
name=k)
for k, shape in self.policy.state_info_specs
}
state_info_vars_list = [
state_info_vars[k] for k in self.policy.state_info_keys
]
if is_recurrent:
valid_var = tf.placeholder(tf.float32,
shape=[None, None],
name="valid")
else:
valid_var = None
dist_info_vars = self.policy.dist_info_sym(obs_var, state_info_vars)
kl = dist.kl_sym(old_dist_info_vars, dist_info_vars)
lr = dist.likelihood_ratio_sym(action_var, old_dist_info_vars,
dist_info_vars)
if self.pol_ent_wt > 0:
if 'log_std' in dist_info_vars:
log_std = dist_info_vars['log_std']
ent = tf.reduce_sum(log_std +
tf.log(tf.sqrt(2 * np.pi * np.e)),
reduction_indices=-1)
elif 'prob' in dist_info_vars:
prob = dist_info_vars['prob']
ent = -tf.reduce_sum(prob * tf.log(prob), reduction_indices=-1)
else:
raise NotImplementedError()
ent = tf.stop_gradient(ent)
adv = advantage_var + self.pol_ent_wt * ent
else:
adv = advantage_var
if is_recurrent:
mean_kl = tf.reduce_sum(kl * valid_var) / tf.reduce_sum(valid_var)
surr_loss = -tf.reduce_sum(
lr * adv * valid_var) / tf.reduce_sum(valid_var)
else:
mean_kl = tf.reduce_mean(kl)
surr_loss = -tf.reduce_mean(lr * adv)
input_list = [
obs_var,
action_var,
advantage_var,
] + state_info_vars_list + old_dist_info_vars_list
if is_recurrent:
input_list.append(valid_var)
self.optimizer.update_opt(loss=surr_loss,
target=self.policy,
leq_constraint=(mean_kl, self.step_size),
inputs=input_list,
constraint_name="mean_kl")
return dict()
def init_opt(self):
with tf.variable_scope("target_policy"):
target_policy = Serializable.clone(self.policy)
oracle_policy = self.oracle_policy
with tf.variable_scope("target_qf"):
target_qf = Serializable.clone(self.qf)
with tf.variable_scope("target_gate_qf"):
target_gate_qf = Serializable.clone(self.gate_qf)
obs = self.obs = self.env.observation_space.new_tensor_variable(
'obs',
extra_dims=1,
)
action = self.env.action_space.new_tensor_variable(
'action',
extra_dims=1,
)
discrete_action = tensor_utils.new_tensor(
'discrete_action',
ndim=2,
dtype=tf.float32,
)
yvar = tensor_utils.new_tensor(
'ys',
ndim=1,
dtype=tf.float32,
)
qf_weight_decay_term = 0.5 * self.qf_weight_decay * \
sum([tf.reduce_sum(tf.square(param)) for param in
self.qf.get_params(regularizable=True)])
policy_weight_decay_term = 0.5 * self.policy_weight_decay * \
sum([tf.reduce_sum(tf.square(param))
for param in self.policy.get_params(regularizable=True)])
policy_qval_novice = self.qf.get_qval_sym(
obs, self.policy.get_novice_policy_sym(obs), deterministic=True)
policy_qval_gate = self.discrete_qf.get_qval_sym(
obs,
self.policy.get_action_binary_gate_sym(obs),
deterministic=True)
qval = self.qf.get_qval_sym(obs, action)
qf_loss = tf.reduce_mean(tf.square(yvar - qval))
qf_reg_loss = qf_loss + qf_weight_decay_term
discrete_qval = self.gate_qf.get_qval_sym(obs, discrete_action)
discrete_qf_loss = tf.reduce_mean(tf.square(yvar - discrete_qval))
discrete_qf_reg_loss = discrete_qf_loss + qf_weight_decay_term
qf_input_list = [yvar, obs, action]
discrete_qf_input_list = [yvar, obs, discrete_action]
policy_input_list = [obs]
policy_gate_input_list = [obs]
gating_network = self.policy.get_action_binary_gate_sym(obs)
policy_surr = -tf.reduce_mean(policy_qval_novice)
policy_reg_surr = policy_surr + policy_weight_decay_term
policy_gate_surr = -tf.reduce_mean(
policy_qval_gate) + policy_weight_decay_term
policy_reg_gate_surr = policy_gate_surr + policy_weight_decay_term
self.qf_update_method.update_opt(loss=qf_reg_loss,
target=self.qf,
inputs=qf_input_list)
self.gate_qf_update_method.update_opt(loss=discrete_qf_reg_loss,
target=self.gate_qf,
inputs=discrete_qf_input_list)
self.policy_update_method.update_opt(loss=policy_reg_surr,
target=self.policy,
inputs=policy_input_list)
self.policy_gate_update_method.update_opt(
loss=policy_reg_gate_surr,
target=self.policy,
inputs=policy_gate_input_list)
f_train_qf = tensor_utils.compile_function(
inputs=qf_input_list,
outputs=[qf_loss, qval, self.qf_update_method._train_op],
)
f_train_discrete_qf = tensor_utils.compile_function(
inputs=discrete_qf_input_list,
outputs=[
discrete_qf_loss, discrete_qval,
self.gate_qf_update_method._train_op
],
)
f_train_policy = tensor_utils.compile_function(
inputs=policy_input_list,
outputs=[policy_surr, self.policy_update_method._train_op],
)
f_train_policy_gate = tensor_utils.compile_function(
inputs=policy_gate_input_list,
outputs=[
policy_gate_surr, self.policy_gate_update_method._train_op,
gating_network
],
)
self.opt_info = dict(
f_train_qf=f_train_qf,
f_train_discrete_qf=f_train_discrete_qf,
f_train_policy=f_train_policy,
f_train_policy_gate=f_train_policy_gate,
target_qf=target_qf,
target_gate_qf=target_gate_qf,
target_policy=target_policy,
oracle_policy=oracle_policy,
)
def init_opt(self):
is_recurrent = int(self.policy.recurrent)
# lists over the meta_batch_size0
context = tf.reshape(self.irl_model.reparam_latent_tile, [
self.meta_batch_size, -1, self.irl_model.T,
self.irl_model.latent_dim
])
# if not self.train_irl:
# context = tf.stop_gradient(context)
obs_vars = self.env.observation_space.new_tensor_variable(
'obs',
extra_dims=1 + is_recurrent,
)
action_vars = self.env.action_space.new_tensor_variable(
'action',
extra_dims=1 + is_recurrent,
)
advantage_vars = tensor_utils.new_tensor(
'advantage',
ndim=1 + is_recurrent,
dtype=tf.float32,
)
clean_obs_vars = tf.placeholder(
tf.float32,
shape=[None] * (1 + is_recurrent) +
[self.env.observation_space.flat_dim - self.irl_model.latent_dim],
name='clean_obs')
policy_input = tf.reshape(
tf.concat([
tf.reshape(clean_obs_vars, [
self.meta_batch_size, -1, self.irl_model.T,
self.env.observation_space.flat_dim -
self.irl_model.latent_dim
]), context
],
axis=-1), [-1, self.env.observation_space.flat_dim])
# input_list = obs_vars + action_vars + advantage_vars
input_list = [clean_obs_vars] + [action_vars] + [advantage_vars] + [
self.irl_model.expert_traj_var
]
dist = self.policy.distribution
old_dist_info_vars_list, state_info_vars_list = [], []
old_dist_info_vars = {
k: tf.placeholder(tf.float32,
shape=[None] * (1 + is_recurrent) + list(shape),
name='old_%s' % k)
for k, shape in dist.dist_info_specs
}
old_dist_info_vars_list += [
old_dist_info_vars[k] for k in dist.dist_info_keys
]
state_info_vars = {
k: tf.placeholder(tf.float32,
shape=[None] * (1 + is_recurrent) + list(shape),
name='%s' % k)
for k, shape in self.policy.state_info_specs
}
state_info_vars_list += [
state_info_vars[k] for k in self.policy.state_info_keys
]
if is_recurrent:
valid_vars = tf.placeholder(tf.float32,
shape=[None, None],
name="valid")
else:
valid_vars = None
surr_losses, mean_kls = [], []
# dist_info_vars = self.policy.dist_info_sym(obs_vars[i], state_info_vars[i])
dist_info_vars = self.policy.dist_info_sym(policy_input,
state_info_vars)
kl = dist.kl_sym(old_dist_info_vars, dist_info_vars)
lr = dist.likelihood_ratio_sym(action_vars, old_dist_info_vars,
dist_info_vars)
if self.pol_ent_wt > 0:
if 'log_std' in dist_info_vars:
log_std = dist_info_vars['log_std']
ent = tf.reduce_sum(log_std +
tf.log(tf.sqrt(2 * np.pi * np.e)),
reduction_indices=-1)
elif 'prob' in dist_info_vars:
prob = dist_info_vars['prob']
ent = -tf.reduce_sum(prob * tf.log(prob), reduction_indices=-1)
else:
raise NotImplementedError()
ent = tf.stop_gradient(ent)
adv = advantage_vars + self.pol_ent_wt * ent
else:
adv = advantage_vars
if is_recurrent:
mean_kl = tf.reduce_sum(
kl * valid_vars) / tf.reduce_sum(valid_vars)
surr_loss = -tf.reduce_sum(
lr * adv * valid_vars) / tf.reduce_sum(valid_vars)
else:
mean_kl = tf.reduce_mean(kl)
surr_loss = -tf.reduce_mean(lr * adv)
surr_losses.append(surr_loss)
mean_kls.append(mean_kl)
surr_loss = tf.reduce_mean(tf.stack(
surr_losses, 0)) # mean over meta_batch_size (the diff tasks)
mean_kl = tf.reduce_mean(tf.stack(mean_kls))
input_list += state_info_vars_list + old_dist_info_vars_list
if is_recurrent:
input_list += valid_vars
self.optimizer.update_opt(loss=surr_loss,
target=self.policy,
leq_constraint=(mean_kl, self.step_size),
inputs=input_list,
constraint_name="mean_kl")
return dict()
def init_opt(self):
is_recurrent = int(self.policy.recurrent)
obs_var = self.env.observation_space.new_tensor_variable(
'obs',
extra_dims=1 + is_recurrent,
)
action_var = self.env.action_space.new_tensor_variable(
'action',
extra_dims=1 + is_recurrent,
)
advantage_var = tensor_utils.new_tensor(
'advantage',
ndim=1 + is_recurrent,
dtype=tf.float32,
)
input_list = [
obs_var,
action_var,
advantage_var,
]
dist = self.policy.distribution
old_dist_info_vars = {
k: tf.placeholder(tf.float32, shape=[None] * (1 + is_recurrent) + list(shape), name='old_%s' % k)
for k, shape in dist.dist_info_specs
}
old_dist_info_vars_list = [old_dist_info_vars[k] for k in dist.dist_info_keys]
state_info_vars = {
k: tf.placeholder(tf.float32, shape=[None] * (1 + is_recurrent) + list(shape), name=k)
for k, shape in self.policy.state_info_specs
}
state_info_vars_list = [state_info_vars[k] for k in self.policy.state_info_keys]
if is_recurrent:
valid_var = tf.placeholder(tf.float32, shape=[None, None], name="valid")
else:
valid_var = None
dist_info_vars = self.policy.dist_info_sym(obs_var, state_info_vars)
kl = dist.kl_sym(old_dist_info_vars, dist_info_vars)
lr = dist.likelihood_ratio_sym(action_var, old_dist_info_vars, dist_info_vars)
if self.pol_ent_wt > 0:
if 'log_std' in dist_info_vars:
log_std = dist_info_vars['log_std']
ent = tf.reduce_sum(log_std + tf.log(tf.sqrt(2 * np.pi * np.e)), reduction_indices=-1)
elif 'prob' in dist_info_vars:
prob = dist_info_vars['prob']
ent = -tf.reduce_sum(prob*tf.log(prob), reduction_indices=-1)
else:
raise NotImplementedError()
ent = tf.stop_gradient(ent)
adv = advantage_var + self.pol_ent_wt*ent
else:
adv = advantage_var
if is_recurrent:
mean_kl = tf.reduce_sum(kl * valid_var) / tf.reduce_sum(valid_var)
surr_loss = - tf.reduce_sum(lr * adv * valid_var) / tf.reduce_sum(valid_var)
else:
mean_kl = tf.reduce_mean(kl)
surr_loss = - tf.reduce_mean(lr * adv)
input_list += state_info_vars_list + old_dist_info_vars_list
if is_recurrent:
input_list.append(valid_var)
self.optimizer.update_opt(
loss=surr_loss,
target=self.policy,
leq_constraint=(mean_kl, self.step_size),
inputs=input_list,
constraint_name="mean_kl"
)
return dict()
def init_opt(self):
# First, create "target" policy and Q functions
with tf.variable_scope("target_policy"):
target_policy = Serializable.clone(self.policy)
with tf.variable_scope("target_qf"):
target_qf = Serializable.clone(self.qf)
# y need to be computed first
obs = self.env.observation_space.new_tensor_variable(
'obs',
extra_dims=1,
)
# The yi values are computed separately as above and then passed to
# the training functions below
action = self.env.action_space.new_tensor_variable(
'action',
extra_dims=1,
)
yvar = tensor_utils.new_tensor(
'ys',
ndim=1,
dtype=tf.float32,
)
qf_weight_decay_term = 0.5 * self.qf_weight_decay * \
sum([tf.reduce_sum(tf.square(param)) for param in
self.qf.get_params(regularizable=True)])
qval = self.qf.get_qval_sym(obs, action)
qf_loss = tf.reduce_mean(tf.square(yvar - qval))
qf_reg_loss = qf_loss + qf_weight_decay_term
policy_weight_decay_term = 0.5 * self.policy_weight_decay * \
sum([tf.reduce_sum(tf.square(param))
for param in self.policy.get_params(regularizable=True)])
policy_qval = self.qf.get_qval_sym(obs,
self.policy.get_action_sym(obs),
deterministic=True)
policy_surr = -tf.reduce_mean(policy_qval)
policy_reg_surr = policy_surr + policy_weight_decay_term
qf_input_list = [yvar, obs, action]
policy_input_list = [obs]
self.qf_update_method.update_opt(loss=qf_reg_loss,
target=self.qf,
inputs=qf_input_list)
self.policy_update_method.update_opt(loss=policy_reg_surr,
target=self.policy,
inputs=policy_input_list)
f_train_qf = tensor_utils.compile_function(
inputs=qf_input_list,
outputs=[qf_loss, qval, self.qf_update_method._train_op],
)
f_train_policy = tensor_utils.compile_function(
inputs=policy_input_list,
outputs=[policy_surr, self.policy_update_method._train_op],
)
self.opt_info = dict(
f_train_qf=f_train_qf,
f_train_policy=f_train_policy,
target_qf=target_qf,
target_policy=target_policy,
)
def init_opt(self):
# First, create "target" policy and Q functions
with tf.variable_scope("target_policy"):
target_policy = Serializable.clone(self.policy)
with tf.variable_scope("target_qf"):
target_qf = Serializable.clone(self.qf)
# y need to be computed first
obs = self.env.observation_space.new_tensor_variable(
'obs',
extra_dims=1,
)
# The yi values are computed separately as above and then passed to
# the training functions below
action = self.env.action_space.new_tensor_variable(
'action',
extra_dims=1,
)
yvar = tensor_utils.new_tensor(
'ys',
ndim=1,
dtype=tf.float32,
)
obs_offpolicy = self.env.observation_space.new_tensor_variable(
'obs_offpolicy',
extra_dims=1,
)
action_offpolicy = self.env.action_space.new_tensor_variable(
'action_offpolicy',
extra_dims=1,
)
yvar = tensor_utils.new_tensor(
'ys',
ndim=1,
dtype=tf.float32,
)
yvar_offpolicy = tensor_utils.new_tensor(
'ys_offpolicy',
ndim=1,
dtype=tf.float32,
)
qf_weight_decay_term = 0.5 * self.qf_weight_decay * \
sum([tf.reduce_sum(tf.square(param)) for param in
self.qf.get_params(regularizable=True)])
qval = self.qf.get_qval_sym(obs, action)
qval_off = self.qf.get_qval_sym(obs_offpolicy, action_offpolicy)
qf_loss = tf.reduce_mean(tf.square(yvar - qval))
qf_loss_off = tf.reduce_mean(tf.square(yvar_offpolicy - qval_off))
# TODO: penalize dramatic changes in gating_func
# if PENALIZE_GATING_DISTRIBUTION_DIVERGENCE:
policy_weight_decay_term = 0.5 * self.policy_weight_decay * \
sum([tf.reduce_sum(tf.square(param))
for param in self.policy.get_params(regularizable=True)])
policy_qval = self.qf.get_qval_sym(obs,
self.policy.get_action_sym(obs),
deterministic=True)
policy_qval_off = self.qf.get_qval_sym(
obs_offpolicy,
self.policy.get_action_sym(obs_offpolicy),
deterministic=True)
policy_surr = -tf.reduce_mean(policy_qval)
policy_surr_off = -tf.reduce_mean(policy_qval_off)
if self.sigma_type == 'unified-gated' or self.sigma_type == 'unified-gated-decaying':
print("Using Gated Sigma!")
input_to_gates = tf.concat([obs, obs_offpolicy], axis=1)
assert input_to_gates.get_shape().as_list()[-1] == obs.get_shape(
).as_list()[-1] + obs_offpolicy.get_shape().as_list()[-1]
# TODO: right now this is a soft-gate, should make a hard-gate (options vs mixtures)
gating_func = MLP(
name="sigma_gate",
output_dim=1,
hidden_sizes=(64, 64),
hidden_nonlinearity=tf.nn.relu,
output_nonlinearity=tf.nn.sigmoid,
input_var=input_to_gates,
input_shape=tuple(
input_to_gates.get_shape().as_list()[1:])).output
elif self.sigma_type == 'unified':
# sample a bernoulli random variable
print("Using Bernoulli sigma!")
gating_func = tf.cast(self.random_dist.sample(qf_loss.get_shape()),
tf.float32)
elif self.sigma_type == 'unified-decaying':
print("Using decaying sigma!")
gating_func = tf.train.exponential_decay(1.0,
self.train_step,
20,
0.96,
staircase=True)
else:
raise Exception("sigma type not supported")
qf_inputs_list = [
yvar, obs, action, yvar_offpolicy, obs_offpolicy, action_offpolicy,
self.train_step
]
qf_reg_loss = qf_loss * (1.0 - gating_func) + qf_loss_off * (
gating_func) + qf_weight_decay_term
policy_input_list = [obs, obs_offpolicy, self.train_step]
policy_reg_surr = policy_surr * (
1.0 - gating_func) + policy_surr_off * (
gating_func) + policy_weight_decay_term
if self.sigma_type == 'unified-gated-decaying':
print("Adding a decaying factor to gated sigma!")
decaying_factor = tf.train.exponential_decay(.5,
self.train_step,
20,
0.96,
staircase=True)
penalty = decaying_factor * tf.nn.l2_loss(gating_func)
qf_reg_loss += penalty
policy_reg_surr += penalty
self.qf_update_method.update_opt(qf_reg_loss,
target=self.qf,
inputs=qf_inputs_list)
self.policy_update_method.update_opt(policy_reg_surr,
target=self.policy,
inputs=policy_input_list)
f_train_qf = tensor_utils.compile_function(
inputs=qf_inputs_list,
outputs=[qf_loss, qval, self.qf_update_method._train_op],
)
f_train_policy = tensor_utils.compile_function(
inputs=policy_input_list,
outputs=[policy_surr, self.policy_update_method._train_op],
)
self.opt_info = dict(
f_train_qf=f_train_qf,
f_train_policy=f_train_policy,
target_qf=target_qf,
target_policy=target_policy,
)
def init_opt(self, lambda_s=100, lambda_v=10, tau=.5):
with tf.variable_scope("target_policy"):
target_policy = Serializable.clone(self.policy)
oracle_policy = self.oracle_policy
with tf.variable_scope("target_qf"):
target_qf = Serializable.clone(self.qf)
obs = self.obs = self.env.observation_space.new_tensor_variable(
'obs',
extra_dims=1,
)
action = self.env.action_space.new_tensor_variable(
'action',
extra_dims=1,
)
yvar = tensor_utils.new_tensor(
'ys',
ndim=1,
dtype=tf.float32,
)
qf_weight_decay_term = 0.5 * self.qf_weight_decay * \
sum([tf.reduce_sum(tf.square(param)) for param in
self.qf.get_params(regularizable=True)])
qval = self.qf.get_qval_sym(obs, action)
qf_loss = tf.reduce_mean(tf.square(yvar - qval))
qf_reg_loss = qf_loss + qf_weight_decay_term
policy_weight_decay_term = 0.5 * self.policy_weight_decay * \
sum([tf.reduce_sum(tf.square(param))
for param in self.policy.get_params(regularizable=True)])
qf_input_list = [yvar, obs, action]
policy_input_list = [obs]
obs_oracle = self.env.observation_space.new_tensor_variable(
'obs_oracle',
extra_dims=1,
)
action_oracle = self.env.action_space.new_tensor_variable(
'action_oracle',
extra_dims=1,
)
yvar_oracle = tensor_utils.new_tensor(
'ys_oracle',
ndim=1,
dtype=tf.float32,
)
qval_oracle = self.qf.get_qval_sym(obs_oracle, action_oracle)
qf_loss_oracle = tf.reduce_mean(tf.square(yvar_oracle - qval_oracle))
qf_reg_loss_oracle = qf_loss_oracle + qf_weight_decay_term
policy_qval_novice = self.qf.get_qval_sym(
obs, self.policy.get_novice_policy_sym(obs), deterministic=True)
gating_network = self.policy.get_action_binary_gate_sym(obs)
policy_qval_oracle = self.qf.get_qval_sym(
obs, self.policy.get_action_oracle_sym(obs), deterministic=True)
combined_losses = tf.concat([
tf.reshape(policy_qval_novice, [-1, 1]),
tf.reshape(policy_qval_oracle, [-1, 1])
],
axis=1)
combined_loss = -tf.reduce_mean(tf.reshape(
tf.reduce_mean(combined_losses * gating_network, axis=1), [-1, 1]),
axis=0)
lambda_s_loss = tf.constant(0.0)
if lambda_s > 0.0:
lambda_s_loss = lambda_s * (tf.reduce_mean(
(tf.reduce_mean(gating_network, axis=0) - tau)**
2) + tf.reduce_mean(
(tf.reduce_mean(gating_network, axis=1) - tau)**2))
lambda_v_loss = tf.constant(0.0)
if lambda_v > 0.0:
mean0, var0 = tf.nn.moments(gating_network, axes=[0])
mean, var1 = tf.nn.moments(gating_network, axes=[1])
lambda_v_loss = -lambda_v * (tf.reduce_mean(var0) +
tf.reduce_mean(var1))
combined_losses = tf.concat([
tf.reshape(policy_qval_novice, [-1, 1]),
tf.reshape(policy_qval_oracle, [-1, 1])
],
axis=1)
combined_loss = -tf.reduce_mean(tf.reshape(
tf.reduce_mean(combined_losses * gating_network, axis=1), [-1, 1]),
axis=0)
lambda_s_loss = tf.constant(0.0)
if lambda_s > 0.0:
lambda_s_loss = lambda_s * (tf.reduce_mean(
(tf.reduce_mean(gating_network, axis=0) - tau)**
2) + tf.reduce_mean(
(tf.reduce_mean(gating_network, axis=1) - tau)**2))
lambda_v_loss = tf.constant(0.0)
if lambda_v > 0.0:
mean0, var0 = tf.nn.moments(gating_network, axes=[0])
mean, var1 = tf.nn.moments(gating_network, axes=[1])
lambda_v_loss = -lambda_v * (tf.reduce_mean(var0) +
tf.reduce_mean(var1))
policy_surr = combined_loss
policy_reg_surr = combined_loss + policy_weight_decay_term + lambda_s_loss + lambda_v_loss
gf_input_list = [obs_oracle, action_oracle, yvar_oracle
] + qf_input_list
self.qf_update_method.update_opt(loss=qf_reg_loss,
target=self.qf,
inputs=qf_input_list)
self.policy_update_method.update_opt(loss=policy_reg_surr,
target=self.policy,
inputs=policy_input_list)
f_train_qf = tensor_utils.compile_function(
inputs=qf_input_list,
outputs=[qf_loss, qval, self.qf_update_method._train_op],
)
f_train_policy = tensor_utils.compile_function(
inputs=policy_input_list,
outputs=[
policy_surr, self.policy_update_method._train_op,
gating_network
],
)
self.opt_info = dict(
f_train_qf=f_train_qf,
f_train_policy=f_train_policy,
target_qf=target_qf,
target_policy=target_policy,
oracle_policy=oracle_policy,
)
def init_opt(self):
###############################
#
# Variable Definitions
#
###############################
all_task_dist_info_vars = []
all_obs_vars = []
for i, policy in enumerate(self.local_policies):
task_obs_var = self.env_partitions[
i].observation_space.new_tensor_variable('obs%d' % i,
extra_dims=1)
task_dist_info_vars = []
for j, other_policy in enumerate(self.local_policies):
state_info_vars = dict() # Not handling recurrent policies
dist_info_vars = other_policy.dist_info_sym(
task_obs_var, state_info_vars)
task_dist_info_vars.append(dist_info_vars)
all_obs_vars.append(task_obs_var)
all_task_dist_info_vars.append(task_dist_info_vars)
obs_var = self.env.observation_space.new_tensor_variable('obs',
extra_dims=1)
action_var = self.env.action_space.new_tensor_variable('action',
extra_dims=1)
advantage_var = tensor_utils.new_tensor('advantage',
ndim=1,
dtype=tf.float32)
old_dist_info_vars = {
k: tf.placeholder(tf.float32,
shape=[None] + list(shape),
name='old_%s' % k)
for k, shape in self.policy.distribution.dist_info_specs
}
old_dist_info_vars_list = [
old_dist_info_vars[k]
for k in self.policy.distribution.dist_info_keys
]
input_list = [obs_var, action_var, advantage_var
] + old_dist_info_vars_list + all_obs_vars
###############################
#
# Local Policy Optimization
#
###############################
self.optimizers = []
self.metrics = []
for n, policy in enumerate(self.local_policies):
state_info_vars = dict()
dist_info_vars = policy.dist_info_sym(obs_var, state_info_vars)
dist = policy.distribution
kl = dist.kl_sym(old_dist_info_vars, dist_info_vars)
lr = dist.likelihood_ratio_sym(action_var, old_dist_info_vars,
dist_info_vars)
surr_loss = -tf.reduce_mean(lr * advantage_var)
if self.constrain_together:
additional_loss = Metrics.kl_on_others(
n, dist, all_task_dist_info_vars)
else:
additional_loss = tf.constant(0.0)
local_loss = surr_loss + self.penalty * additional_loss
kl_metric = tensor_utils.compile_function(inputs=input_list,
outputs=additional_loss,
log_name="KLPenalty%d" %
n)
self.metrics.append(kl_metric)
mean_kl_constraint = tf.reduce_mean(kl)
optimizer = self.optimizer_class(**self.optimizer_args)
optimizer.update_opt(
loss=local_loss,
target=policy,
leq_constraint=(mean_kl_constraint, self.step_size),
inputs=input_list,
constraint_name="mean_kl_%d" % n,
)
self.optimizers.append(optimizer)
###############################
#
# Global Policy Optimization
#
###############################
# Behaviour Cloning Loss
state_info_vars = dict()
center_dist_info_vars = self.policy.dist_info_sym(
obs_var, state_info_vars)
behaviour_cloning_loss = tf.losses.mean_squared_error(
action_var, center_dist_info_vars['mean'])
self.center_optimizer = FirstOrderOptimizer(max_epochs=1,
verbose=True,
batch_size=1000)
self.center_optimizer.update_opt(behaviour_cloning_loss, self.policy,
[obs_var, action_var])
# TRPO Loss
kl = dist.kl_sym(old_dist_info_vars, center_dist_info_vars)
lr = dist.likelihood_ratio_sym(action_var, old_dist_info_vars,
center_dist_info_vars)
center_trpo_loss = -tf.reduce_mean(lr * advantage_var)
mean_kl_constraint = tf.reduce_mean(kl)
optimizer = self.optimizer_class(**self.optimizer_args)
optimizer.update_opt(
loss=center_trpo_loss,
target=self.policy,
leq_constraint=(mean_kl_constraint, self.step_size),
inputs=[obs_var, action_var, advantage_var] +
old_dist_info_vars_list,
constraint_name="mean_kl_center",
)
self.center_trpo_optimizer = optimizer
# Reset Local Policies to Global Policy
assignment_operations = []
for policy in self.local_policies:
for param_local, param_center in zip(
policy.get_params_internal(),
self.policy.get_params_internal()):
if 'std' not in param_local.name:
assignment_operations.append(
tf.assign(param_local, param_center))
self.reset_to_center = tf.group(*assignment_operations)
return dict()
def init_opt(self):
is_recurrent = int(self.policy.recurrent)
obs_var = self.env.observation_space.new_tensor_variable(
'obs',
extra_dims=1 + is_recurrent,
)
action_var = self.env.action_space.new_tensor_variable(
'action',
extra_dims=1 + is_recurrent,
)
advantage_var = tensor_utils.new_tensor(
name='advantage',
ndim=1 + is_recurrent,
dtype=tf.float32,
)
dist = self.policy.distribution
old_dist_info_vars = {
k: tf.placeholder(tf.float32,
shape=[None] * (1 + is_recurrent) + list(shape),
name='old_%s' % k)
for k, shape in dist.dist_info_specs
}
old_dist_info_vars_list = [
old_dist_info_vars[k] for k in dist.dist_info_keys
]
state_info_vars = {
k: tf.placeholder(tf.float32,
shape=[None] * (1 + is_recurrent) + list(shape),
name=k)
for k, shape in self.policy.state_info_specs
}
state_info_vars_list = [
state_info_vars[k] for k in self.policy.state_info_keys
]
if is_recurrent:
valid_var = tf.placeholder(tf.float32,
shape=[None, None],
name="valid")
else:
valid_var = None
dist_info_vars = self.policy.dist_info_sym(obs_var, state_info_vars)
logli = dist.log_likelihood_sym(action_var, dist_info_vars)
kl = dist.kl_sym(old_dist_info_vars, dist_info_vars)
# formulate as a minimization problem
# The gradient of the surrogate objective is the policy gradient
if is_recurrent:
surr_obj = -tf.reduce_sum(
logli * advantage_var * valid_var) / tf.reduce_sum(valid_var)
mean_kl = tf.reduce_sum(kl * valid_var) / tf.reduce_sum(valid_var)
max_kl = tf.reduce_max(kl * valid_var)
else:
surr_obj = -tf.reduce_mean(logli * advantage_var)
mean_kl = tf.reduce_mean(kl)
max_kl = tf.reduce_max(kl)
input_list = [obs_var, action_var, advantage_var
] + state_info_vars_list
if is_recurrent:
input_list.append(valid_var)
vars_info = {
"mean_kl": mean_kl,
"input_list": input_list,
"obs_var": obs_var,
"action_var": action_var,
"advantage_var": advantage_var,
"surr_loss": surr_obj,
"dist_info_vars": dist_info_vars,
"lr": logli,
}
if self.qprop:
eta_var = tensor_utils.new_tensor(
'eta',
ndim=1 + is_recurrent,
dtype=tf.float32,
)
qvalue = self.qf.get_e_qval_sym(vars_info["obs_var"],
self.policy,
deterministic=True)
qprop_surr_loss = -tf.reduce_mean(
vars_info["lr"] * vars_info["advantage_var"]) - tf.reduce_mean(
qvalue * eta_var)
input_list += [eta_var]
self.optimizer.update_opt(
loss=qprop_surr_loss,
target=self.policy,
inputs=input_list,
)
control_variate = self.qf.get_cv_sym(obs_var, action_var,
self.policy)
f_control_variate = tensor_utils.compile_function(
inputs=[obs_var, action_var],
outputs=control_variate,
)
self.opt_info_qprop = dict(f_control_variate=f_control_variate, )
else:
self.optimizer.update_opt(loss=surr_obj,
target=self.policy,
inputs=input_list)
f_kl = tensor_utils.compile_function(
inputs=input_list + old_dist_info_vars_list,
outputs=[mean_kl, max_kl],
)
self.opt_info = dict(
f_kl=f_kl,
target_policy=self.policy,
)
self.init_opt_critic()
def init_opt(self, name=''):
is_recurrent = int(self.policy.recurrent)
obs_var = self.env.observation_space.new_tensor_variable(
name + 'obs',
extra_dims=1 + is_recurrent,
)
action_var = self.env.action_space.new_tensor_variable(
name + 'action',
extra_dims=1 + is_recurrent,
)
advantage_var = tensor_utils.new_tensor(
name + 'advantage',
ndim=1 + is_recurrent,
dtype=tf.float32,
)
dist = self.policy.distribution
old_dist_info_vars = {
k: tf.placeholder(tf.float32, shape=[None] * (1 + is_recurrent) + list(shape), name=name+'old_%s' % k)
for k, shape in dist.dist_info_specs
}
old_dist_info_vars_list = [old_dist_info_vars[k] for k in dist.dist_info_keys]
state_info_vars = {
k: tf.placeholder(tf.float32, shape=[None] * (1 + is_recurrent) + list(shape), name=name+k)
for k, shape in self.policy.state_info_specs
}
state_info_vars_list = [state_info_vars[k] for k in self.policy.state_info_keys]
if is_recurrent:
valid_var = tf.placeholder(tf.float32, shape=[None, None], name=name+"valid")
else:
valid_var = None
input_list = [
obs_var,
action_var,
advantage_var,
] + state_info_vars_list + old_dist_info_vars_list
if is_recurrent:
input_list.append(valid_var)
if self.kl_sample_backups > 0:
kl_obs_var = self.env.observation_space.new_tensor_variable(
name + 'kl_obs',
extra_dims=1 + is_recurrent,
)
kl_old_dist_info_vars = {
k: tf.placeholder(tf.float32, shape=[None] * (1 + is_recurrent) + list(shape), name=name+'kl_old_%s' % k)
for k, shape in dist.dist_info_specs
}
kl_old_dist_info_vars_list = [kl_old_dist_info_vars[k] for k in dist.dist_info_keys]
kl_state_info_vars = {
k: tf.placeholder(tf.float32, shape=[None] * (1 + is_recurrent) + list(shape), name=name+'kl_%s'%k)
for k, shape in self.policy.state_info_specs
}
kl_state_info_vars_list = [kl_state_info_vars[k] for k in self.policy.state_info_keys]
kl_dist_info_vars = self.policy.dist_info_sym(kl_obs_var, kl_state_info_vars)
kl = dist.kl_sym(kl_old_dist_info_vars, kl_dist_info_vars)
input_list += [kl_obs_var] + kl_state_info_vars_list + kl_old_dist_info_vars_list
dist_info_vars = self.policy.dist_info_sym(obs_var, state_info_vars)
else:
dist_info_vars = self.policy.dist_info_sym(obs_var, state_info_vars)
kl = dist.kl_sym(old_dist_info_vars, dist_info_vars)
lr = dist.likelihood_ratio_sym(action_var, old_dist_info_vars, dist_info_vars)
if self.qprop:
if is_recurrent: raise NotImplementedError
eta_var = tensor_utils.new_tensor(
'eta',
ndim=1 + is_recurrent,
dtype=tf.float32,
)
surr_loss = -tf.reduce_mean(lr * advantage_var)
if self.qprop_nu > 0: surr_loss *= 1-self.qprop_nu
if self.sample_backups > 0 or not self.policy_sample_last:
off_obs_var = self.env.observation_space.new_tensor_variable(
name + 'off_obs',
extra_dims=1 + is_recurrent,
)
off_e_qval = self.qf.get_e_qval_sym(off_obs_var, self.policy, deterministic=True)
input_list += [off_obs_var]
surr_loss -= tf.reduce_mean(off_e_qval)# * eta_var)
else:
e_qval = self.qf.get_e_qval_sym(obs_var, self.policy, deterministic=True)
surr_loss -= tf.reduce_mean(e_qval * eta_var)
mean_kl = tf.reduce_mean(kl)
input_list += [eta_var]
control_variate = self.qf.get_cv_sym(obs_var,
action_var, self.policy)
f_control_variate = tensor_utils.compile_function(
inputs=[obs_var, action_var],
outputs=control_variate,
)
self.opt_info_qprop = dict(
f_control_variate=f_control_variate,
)
elif self.phi:
# Using stein control functional variate reduction
if is_recurrent: raise NotImplementedError
eta_var = tensor_utils.new_tensor(
'eta',
ndim = 1 + is_recurrent,
dtype=tf.float32,
)
if isinstance(self.pf, ContinuousLinearPhiFunction):
phival = self.pf.get_e_phival_sym(obs_var, self.policy,
gradwrtmu=True, deterministic=True)
surr_loss = -tf.reduce_mean(lr * advantage_var) - \
tf.reduce_mean(phival * eta_var)
stein_phi = self.pf.get_phi_bar_sym(obs_var,
action_var, self.policy)
elif isinstance(self.pf, ContinuousQuadraticPhiFunction):
dist_info = self.policy.dist_info_sym(obs_var)
mean = dist_info["mean"]
log_std = dist_info["log_std"]
phi_derives = self.pf.get_phi_derive_sym(obs_var, action_var)
surr_loss= -tf.reduce_mean(lr * advantage_var)
mu_loss = - tf.reduce_sum(tf.stop_gradient(tf.expand_dims(lr * eta_var, axis=1) * \
phi_derives['phi_prime']) * mean, axis=1)
var_loss = - tf.reduce_sum(tf.stop_gradient(tf.expand_dims(lr * eta_var, axis=1) * \
phi_derives['phi_double_prime']) * tf.exp(2.*log_std),axis=1)
surr_loss = surr_loss + tf.reduce_mean(mu_loss) + \
tf.reduce_mean(var_loss)
stein_phi = self.pf.get_phival_sym(obs_var, action_var)
elif isinstance(self.pf, ContinuousMLPPhiFunction):
dist_info = self.policy.dist_info_sym(obs_var)
mean = dist_info['mean']
log_std = dist_info['log_std']
grad_info, _ = self.policy.get_grad_info_sym(obs_var, action_var)
phi_derives = self.pf.get_phi_derive_sym(obs_var, action_var)
surr_loss = -tf.reduce_mean(lr * advantage_var)
mu_loss = - tf.reduce_sum(tf.stop_gradient(tf.expand_dims(lr * eta_var, axis=1) * \
phi_derives['phi_prime']) * mean, axis=1)
var_loss = -(- tf.reduce_sum(tf.stop_gradient(tf.expand_dims(lr * eta_var, axis=1) * \
.5 * grad_info['logpi_dmu'] * \
phi_derives['phi_prime']) * tf.exp(2.*log_std), axis=1))
surr_loss = surr_loss + tf.reduce_mean(mu_loss) + \
tf.reduce_mean(var_loss)
stein_phi = self.pf.get_phival_sym(obs_var, action_var)
else:
raise NotImplementedError
mean_kl = tf.reduce_mean(kl)
input_list += [eta_var]
f_stein_phi = tensor_utils.compile_function(
inputs=[obs_var, action_var],
outputs=stein_phi,
)
self.opt_info_phi=dict(
f_stein_phi=f_stein_phi
)
elif not self.qprop and not self.phi:
if is_recurrent:
mean_kl = tf.reduce_sum(kl * valid_var) / tf.reduce_sum(valid_var)
surr_loss = - tf.reduce_sum(lr * advantage_var * valid_var) / tf.reduce_sum(valid_var)
else:
mean_kl = tf.reduce_mean(kl)
surr_loss = - tf.reduce_mean(lr * advantage_var)
if self.ac_delta > 0:
ac_obs_var = self.env.observation_space.new_tensor_variable(
name + 'ac_obs',
extra_dims=1 + is_recurrent,
)
e_qval = self.qf.get_e_qval_sym(ac_obs_var, self.policy, deterministic=True)
input_list += [ac_obs_var]
surr_loss *= (1.0 - self.ac_delta)
surr_loss -= self.ac_delta * tf.reduce_mean(e_qval)
self.optimizer.update_opt(
loss=surr_loss,
target=self.policy,
leq_constraint=(mean_kl, self.step_size),
inputs=input_list,
constraint_name="mean_kl"
)
self.opt_info = dict(
target_policy=self.policy,
)
self.init_opt_critic()
self.init_opt_phi()
return dict()
def init_opt(self):
is_recurrent = int(self.policy.recurrent)
obs_var = self.env.observation_space.new_tensor_variable(
'obs',
extra_dims=1 + is_recurrent,
)
action_var = self.env.action_space.new_tensor_variable(
'action',
extra_dims=1 + is_recurrent,
)
advantage_var = tensor_utils.new_tensor(
'advantage',
ndim=1 + is_recurrent,
dtype=tf.float32,
)
dist = self.policy.distribution
old_dist_info_vars = {
k: tf.placeholder(tf.float32, shape=(None,) * (1 + is_recurrent) + shape, name='old_%s' % k)
for k, shape in dist.dist_info_specs
}
old_dist_info_vars_list = [old_dist_info_vars[k] for k in dist.dist_info_keys]
state_info_vars = {
k: tf.placeholder(tf.float32, shape=(None,) * (1 + is_recurrent) + shape, name=k)
for k, shape in self.policy.state_info_specs
}
state_info_vars_list = [state_info_vars[k] for k in self.policy.state_info_keys]
kl_penalty_var = tf.Variable(
initial_value=self.initial_kl_penalty,
dtype=tf.float32,
name="kl_penalty"
)
if is_recurrent:
valid_var = tf.placeholder(tf.float32, shape=(None, None), name="valid")
if hasattr(self.policy, "prob_network"):
rnn_network = self.policy.prob_network
state_dim = rnn_network.state_dim
recurrent_layer = rnn_network.recurrent_layer
state_init_param = rnn_network.state_init_param
elif hasattr(self.policy, "head_network"):
rnn_network = self.policy.head_network
state_dim = rnn_network.state_dim
recurrent_layer = rnn_network.recurrent_layer
state_init_param = rnn_network.state_init_param
else:
state_dim = self.policy.l_rnn.state_dim
recurrent_layer = self.policy.l_rnn
state_init_param = tf.reshape(self.policy.l_rnn.cell.zero_state(1, dtype=tf.float32), (-1,))
state_var = tf.placeholder(tf.float32, (None, state_dim), "state")
recurrent_state_output = dict()
minibatch_dist_info_vars = self.policy.dist_info_sym(
obs_var, state_info_vars,
recurrent_state={recurrent_layer: state_var},
recurrent_state_output=recurrent_state_output,
)
state_output = recurrent_state_output[recurrent_layer]
if hasattr(self.policy, "prob_network") or hasattr(self.policy, "head_network"):
final_state = tf.reverse(state_output, [1])[:, 0, :]
else:
final_state = state_output
lr = dist.likelihood_ratio_sym(action_var, old_dist_info_vars, minibatch_dist_info_vars)
kl = dist.kl_sym(old_dist_info_vars, minibatch_dist_info_vars)
ent = tf.reduce_sum(dist.entropy_sym(minibatch_dist_info_vars) * valid_var) / tf.reduce_sum(valid_var)
mean_kl = tf.reduce_sum(kl * valid_var) / tf.reduce_sum(valid_var)
clipped_lr = tf.clip_by_value(lr, 1. - self.clip_lr, 1. + self.clip_lr)
surr_loss = - tf.reduce_sum(lr * advantage_var * valid_var) / tf.reduce_sum(valid_var)
clipped_surr_loss = - tf.reduce_sum(
tf.minimum(lr * advantage_var, clipped_lr * advantage_var) * valid_var
) / tf.reduce_sum(valid_var)
clipped_surr_pen_loss = clipped_surr_loss - self.entropy_bonus_coeff * ent
if self.use_kl_penalty:
clipped_surr_pen_loss += kl_penalty_var * tf.maximum(0., mean_kl - self.step_size)
self.optimizer.update_opt(
loss=clipped_surr_pen_loss,
target=self.policy,
inputs=[obs_var, action_var, advantage_var] + state_info_vars_list + old_dist_info_vars_list + [
valid_var],
rnn_init_state=state_init_param,
rnn_state_input=state_var,
rnn_final_state=final_state,
diagnostic_vars=OrderedDict([
("UnclippedSurrLoss", surr_loss),
("MeanKL", mean_kl),
])
)
else:
dist_info_vars = self.policy.dist_info_sym(obs_var, state_info_vars)
lr = dist.likelihood_ratio_sym(action_var, old_dist_info_vars, dist_info_vars)
kl = dist.kl_sym(old_dist_info_vars, dist_info_vars)
ent = tf.reduce_mean(dist.entropy_sym(dist_info_vars))
mean_kl = tf.reduce_mean(kl)
clipped_lr = tf.clip_by_value(lr, 1. - self.clip_lr, 1. + self.clip_lr)
surr_loss = - tf.reduce_mean(lr * advantage_var)
clipped_surr_loss = - tf.reduce_mean(
tf.minimum(lr * advantage_var, clipped_lr * advantage_var)
)
clipped_surr_pen_loss = clipped_surr_loss - self.entropy_bonus_coeff * ent
if self.use_kl_penalty:
clipped_surr_pen_loss += kl_penalty_var * tf.maximum(0., mean_kl - self.step_size)
self.optimizer.update_opt(
loss=clipped_surr_pen_loss,
target=self.policy,
inputs=[obs_var, action_var, advantage_var] + state_info_vars_list + old_dist_info_vars_list,
diagnostic_vars=OrderedDict([
("UnclippedSurrLoss", surr_loss),
("MeanKL", mean_kl),
])
)
self.kl_penalty_var = kl_penalty_var
self.f_increase_penalty = tensor_utils.compile_function(
inputs=[],
outputs=tf.assign(
kl_penalty_var,
tf.minimum(kl_penalty_var * self.increase_penalty_factor, self.max_penalty)
)
)
self.f_decrease_penalty = tensor_utils.compile_function(
inputs=[],
outputs=tf.assign(
kl_penalty_var,
tf.maximum(kl_penalty_var * self.decrease_penalty_factor, self.min_penalty)
)
)
self.f_reset_penalty = tensor_utils.compile_function(
inputs=[],
outputs=tf.assign(
kl_penalty_var,
self.initial_kl_penalty
)
)
def init_experts_opt(self):
###############################
#
# Variable Definitions
#
###############################
all_task_dist_info_vars = []
all_obs_vars = []
for i, policy in enumerate(self.local_policies):
task_obs_var = self.env_partitions[
i].observation_space.new_tensor_variable('obs%d' % i,
extra_dims=1)
task_dist_info_vars = []
for j, other_policy in enumerate(self.local_policies):
state_info_vars = dict() # Not handling recurrent policies
dist_info_vars = other_policy.dist_info_sym(
task_obs_var, state_info_vars)
task_dist_info_vars.append(dist_info_vars)
all_obs_vars.append(task_obs_var)
all_task_dist_info_vars.append(task_dist_info_vars)
obs_var = self.env.observation_space.new_tensor_variable('obs',
extra_dims=1)
action_var = self.env.action_space.new_tensor_variable('action',
extra_dims=1)
advantage_var = tensor_utils.new_tensor('advantage',
ndim=1,
dtype=tf.float32)
old_dist_info_vars = {
k: tf.placeholder(tf.float32,
shape=[None] + list(shape),
name='old_%s' % k)
for k, shape in self.policy.distribution.dist_info_specs
}
old_dist_info_vars_list = [
old_dist_info_vars[k]
for k in self.policy.distribution.dist_info_keys
]
central_obs_vars = [elem[1] for elem in self.central_policy_dist_infos]
input_list = [
obs_var, action_var, advantage_var
] + old_dist_info_vars_list + all_obs_vars + central_obs_vars
###############################
#
# Local Policy Optimization
#
###############################
self.optimizers = []
self.metrics = []
for n, policy in enumerate(self.local_policies):
state_info_vars = dict()
dist_info_vars = policy.dist_info_sym(obs_var, state_info_vars)
dist = policy.distribution
kl = dist.kl_sym(old_dist_info_vars, dist_info_vars)
lr = dist.likelihood_ratio_sym(action_var, old_dist_info_vars,
dist_info_vars)
surr_loss = -tf.reduce_mean(lr * advantage_var)
if self.constrain_together:
additional_loss = Metrics.kl_on_others(
n, dist, all_task_dist_info_vars)
elif self.constrain_against_central:
additional_loss = Metrics.kl_on_central(
dist, dist_info_vars, self.central_policy_dist_infos[n][0])
else:
additional_loss = tf.constant(0.0)
local_loss = surr_loss + self.penalty * additional_loss
kl_metric = tensor_utils.compile_function(inputs=input_list,
outputs=additional_loss,
log_name="KLPenalty%d" %
n)
self.metrics.append(kl_metric)
mean_kl_constraint = tf.reduce_mean(kl)
optimizer = PenaltyLbfgsOptimizer(name='expertOptimizer_' + str(n))
optimizer.update_opt(
loss=local_loss,
target=policy,
leq_constraint=(mean_kl_constraint, self.step_size),
inputs=input_list,
constraint_name="mean_kl_%d" % n,
)
self.optimizers.append(optimizer)
return dict()
def init_opt(self):
is_recurrent = int(self.policy.recurrent)
obs_var = self.env.observation_space.new_tensor_variable(
'obs',
extra_dims=1 + is_recurrent,
)
action_var = self.env.action_space.new_tensor_variable(
'action',
extra_dims=1 + is_recurrent,
)
advantage_var = tensor_utils.new_tensor(
name='advantage',
ndim=1 + is_recurrent,
dtype=tf.float32,
)
dist = self.policy.distribution
old_dist_info_vars = {
k: tf.placeholder(tf.float32, shape=[None] * (1 + is_recurrent) + list(shape), name='old_%s' % k)
for k, shape in dist.dist_info_specs
}
old_dist_info_vars_list = [old_dist_info_vars[k] for k in dist.dist_info_keys]
state_info_vars = {
k: tf.placeholder(tf.float32, shape=[None] * (1 + is_recurrent) + list(shape), name=k)
for k, shape in self.policy.state_info_specs
}
state_info_vars_list = [state_info_vars[k] for k in self.policy.state_info_keys]
if is_recurrent:
valid_var = tf.placeholder(tf.float32, shape=[None, None], name="valid")
else:
valid_var = None
dist_info_vars = self.policy.dist_info_sym(obs_var, state_info_vars)
logli = dist.log_likelihood_sym(action_var, dist_info_vars)
kl = dist.kl_sym(old_dist_info_vars, dist_info_vars)
# formulate as a minimization problem
# The gradient of the surrogate objective is the policy gradient
if is_recurrent:
surr_obj = - tf.reduce_sum(logli * advantage_var * valid_var) / tf.reduce_sum(valid_var)
mean_kl = tf.reduce_sum(kl * valid_var) / tf.reduce_sum(valid_var)
max_kl = tf.reduce_max(kl * valid_var)
else:
surr_obj = - tf.reduce_mean(logli * advantage_var)
mean_kl = tf.reduce_mean(kl)
max_kl = tf.reduce_max(kl)
input_list = [obs_var, action_var, advantage_var] + state_info_vars_list
if is_recurrent:
input_list.append(valid_var)
self.optimizer.update_opt(loss=surr_obj, target=self.policy, inputs=input_list)
f_kl = tensor_utils.compile_function(
inputs=input_list + old_dist_info_vars_list,
outputs=[mean_kl, max_kl],
)
self.opt_info = dict(
f_kl=f_kl,
)
def init_opt(self):
is_recurrent = int(self.policy.recurrent)
obs_var = self.env.observation_space.new_tensor_variable(
'obs',
extra_dims=1 + is_recurrent,
)
action_var = self.env.action_space.new_tensor_variable(
'action',
extra_dims=1 + is_recurrent,
)
advantage_var = tensor_utils.new_tensor(
'advantage',
ndim=1 + is_recurrent,
dtype=tf.float32,
)
dist = self.policy.distribution
old_dist_info_vars = {
k: tf.placeholder(tf.float32, shape=(None,) * (1 + is_recurrent) + shape, name='old_%s' % k)
for k, shape in dist.dist_info_specs
}
old_dist_info_vars_list = [old_dist_info_vars[k] for k in dist.dist_info_keys]
state_info_vars = {
k: tf.placeholder(tf.float32, shape=(None,) * (1 + is_recurrent) + shape, name=k)
for k, shape in self.policy.state_info_specs
}
state_info_vars_list = [state_info_vars[k] for k in self.policy.state_info_keys]
kl_penalty_var = tf.Variable(
initial_value=self.initial_kl_penalty,
dtype=tf.float32,
name="kl_penalty"
)
# TODO: The code below only works for FF policy.
assert is_recurrent == 0
dist_info_vars = self.policy.dist_info_sym(obs_var, state_info_vars)
lr = dist.likelihood_ratio_sym(action_var, old_dist_info_vars, dist_info_vars)
kl = dist.kl_sym(old_dist_info_vars, dist_info_vars)
ent = tf.reduce_mean(dist.entropy_sym(dist_info_vars))
mean_kl = tf.reduce_mean(kl)
clipped_lr = tf.clip_by_value(lr, 1. - self.clip_lr, 1. + self.clip_lr)
surr_loss = - tf.reduce_mean(lr * advantage_var)
clipped_surr_loss = - tf.reduce_mean(
tf.minimum(lr * advantage_var, clipped_lr * advantage_var)
)
clipped_surr_pen_loss = clipped_surr_loss - self.entropy_bonus_coeff * ent
if self.use_kl_penalty:
clipped_surr_pen_loss += kl_penalty_var * tf.maximum(0., mean_kl - self.step_size)
self.optimizer.update_opt(
loss=clipped_surr_pen_loss,
target=self.policy,
inputs=[obs_var, action_var, advantage_var] + state_info_vars_list + old_dist_info_vars_list,
diagnostic_vars=OrderedDict([
("UnclippedSurrLoss", surr_loss),
("MeanKL", mean_kl),
])
)
self.kl_penalty_var = kl_penalty_var
self.f_increase_penalty = tensor_utils.compile_function(
inputs=[],
outputs=tf.assign(
kl_penalty_var,
tf.minimum(kl_penalty_var * self.increase_penalty_factor, self.max_penalty)
)
)
self.f_decrease_penalty = tensor_utils.compile_function(
inputs=[],
outputs=tf.assign(
kl_penalty_var,
tf.maximum(kl_penalty_var * self.decrease_penalty_factor, self.min_penalty)
)
)
self.f_reset_penalty = tensor_utils.compile_function(
inputs=[],
outputs=tf.assign(
kl_penalty_var,
self.initial_kl_penalty
)
)
return dict()
def init_opt(self):
is_recurrent = int(self.policy.recurrent)
obs_var = self.env.observation_space.new_tensor_variable(
'obs',
extra_dims=1 + is_recurrent,
)
action_var = self.env.action_space.new_tensor_variable(
'action',
extra_dims=1 + is_recurrent,
)
advantage_var = tensor_utils.new_tensor(
'advantage',
ndim=1 + is_recurrent,
dtype=tf.float32,
)
dist = self.policy.distribution
old_dist_info_vars = {
k: tf.placeholder(tf.float32,
shape=[None] * (1 + is_recurrent) + list(shape),
name='old_%s' % k)
for k, shape in dist.dist_info_specs
}
old_dist_info_vars_list = [
old_dist_info_vars[k] for k in dist.dist_info_keys
]
state_info_vars = {
k: tf.placeholder(tf.float32,
shape=[None] * (1 + is_recurrent) + list(shape),
name=k)
for k, shape in self.policy.state_info_specs
}
state_info_vars_list = [
state_info_vars[k] for k in self.policy.state_info_keys
]
if is_recurrent:
valid_var = tf.placeholder(tf.float32,
shape=[None, None],
name="valid")
else:
valid_var = None
dist_info_vars = self.policy.dist_info_sym(obs_var, state_info_vars)
kl = dist.kl_sym(old_dist_info_vars, dist_info_vars)
lr = dist.likelihood_ratio_sym(action_var, old_dist_info_vars,
dist_info_vars)
# entropy_bonus = sum(list(entropy_list[j][i] for j in range(self.num_grad_updates)))
entropy = dist.entropy_sym(dist_info_vars)
clipped_obj = tf.minimum(
lr * advantage_var,
tf.clip_by_value(lr, 1 - self.clip_eps, 1 + self.clip_eps) *
advantage_var)
if is_recurrent:
mean_entropy = tf.reduce_sum(entropy) / tf.reduce_sum(valid_var)
mean_kl = tf.reduce_sum(kl * valid_var) / tf.reduce_sum(valid_var)
surr_loss = - tf.reduce_sum(clipped_obj * valid_var) / tf.reduce_sum(valid_var) \
+ self.kl_coeff * mean_kl - self.entropy_coeff * mean_entropy
else:
mean_entropy = tf.reduce_mean(entropy)
mean_kl = tf.reduce_mean(kl)
surr_loss = -tf.reduce_mean(
clipped_obj
) + self.kl_coeff * mean_kl - self.entropy_coeff * mean_entropy
input_list = [
obs_var,
action_var,
advantage_var,
] + state_info_vars_list + old_dist_info_vars_list
if is_recurrent:
input_list.append(valid_var)
extra_inputs = [tf.placeholder(tf.float32, shape=[], name='kl_coeff')]
self.optimizer.update_opt(loss=surr_loss,
target=self.policy,
kl=mean_kl,
inputs=input_list,
extra_inputs=extra_inputs)
return dict()
def init_opt(self):
observations = self.env.observation_space.new_tensor_variable(
'observations',
extra_dims=1,
)
actions = self.env.action_space.new_tensor_variable(
'action',
extra_dims=1,
)
advantage = tensor_utils.new_tensor(
name='advantage',
ndim=1,
dtype=tf.float32,
)
dist = self.policy.distribution
self.loss = tf.placeholder(tf.float32, name='actor_loss')
self.entropy_loss = tf.placeholder(tf.float32, name='entropy_loss')
self.avg_rewards = tf.placeholder(tf.float32, name='avg_rewards')
self.total_rewards = tf.placeholder(tf.float32, name='total_rewards')
old_dist_info_vars = {
k: tf.placeholder(tf.float32,
shape=[None] * 1 + list(shape),
name='old_%s' % k)
for k, shape in dist.dist_info_specs
}
old_dist_info_vars_list = [
old_dist_info_vars[k] for k in dist.dist_info_keys
]
state_info_vars = {
k: tf.placeholder(tf.float32,
shape=[None] * 1 + list(shape),
name=k)
for k, shape in self.policy.state_info_specs
}
state_info_vars_list = [
state_info_vars[k] for k in self.policy.state_info_keys
]
dist_info_vars = self.policy.dist_info_sym(observations,
state_info_vars)
logli = dist.log_likelihood_sym(actions, dist_info_vars)
kl = dist.kl_sym(old_dist_info_vars, dist_info_vars)
loss = -tf.reduce_mean(logli * advantage)
mean_kl = tf.reduce_mean(kl)
max_kl = tf.reduce_max(kl)
input_list = [observations, actions, advantage] + state_info_vars_list
self.optimizer.update_opt(loss=loss,
target=self.policy,
inputs=input_list)
f_kl = tensor_utils.compile_function(
inputs=input_list + old_dist_info_vars_list,
outputs=[mean_kl, max_kl],
)
self.opt_info = dict(f_kl=f_kl, )
self.writer = tf.train.SummaryWriter("summary/")
self.write_op = tf.merge_summary([
tf.scalar_summary("Loss", self.loss),
tf.scalar_summary("Entropy Loss", self.entropy_loss),
tf.scalar_summary("Total Rewards", self.total_rewards),
tf.scalar_summary("Avg Rewards", self.avg_rewards)
])
