def __init__(self,
actor_critic,
value_loss_coef,
entropy_coef,
lr=None,
eps=None,
alpha=None,
max_grad_norm=None,
acktr=False,
gradient_noise=0.0):
self.actor_critic = actor_critic
self.acktr = acktr
self.value_loss_coef = value_loss_coef
self.entropy_coef = entropy_coef
self.max_grad_norm = max_grad_norm
if acktr:
self.optimizer = KFACOptimizer(actor_critic)
else:
self.optimizer = optim.RMSprop(actor_critic.parameters(),
lr,
eps=eps,
alpha=alpha)
self.gradient_noise = gradient_noise
def __init__(self,
actor_critic,
value_loss_coef,
entropy_coef,
lr=None,
eps=None,
alpha=None,
max_grad_norm=None,
acktr=False):
self.actor_critic = actor_critic
self.acktr = acktr
self.value_loss_coef = value_loss_coef
self.entropy_coef = entropy_coef
self.max_grad_norm = max_grad_norm
# import pdb; pdb.set_trace()
if acktr:
self.optimizer = KFACOptimizer([actor_critic, actor_critic])
else:
self.optimizer = optim.RMSprop(actor_critic.parameters(),
lr,
eps=eps,
alpha=alpha)
def __init__(self,
actor_critic,
value_loss_coef,
entropy_coef,
lr=None,
eps=None,
alpha=None,
max_grad_norm=None,
acktr=False,
train_selfsup_attention=False):
self.actor_critic = actor_critic
self.acktr = acktr
self.value_loss_coef = value_loss_coef
self.entropy_coef = entropy_coef
self.max_grad_norm = max_grad_norm
self.train_selfsup_attention = train_selfsup_attention
if acktr:
self.optimizer = KFACOptimizer(actor_critic)
else:
self.optimizer = optim.RMSprop(actor_critic.parameters(),
lr,
eps=eps,
alpha=alpha)
if self.train_selfsup_attention:
self.selfsup_attention_optimizer = optim.Adam(
actor_critic.base.selfsup_attention.parameters(), 0.001)
def __init__(self,
actor_critic,
value_loss_coef,
entropy_coef,
lr=None,
lr_beta=None,
reg_beta=None,
eps=None,
alpha=None,
max_grad_norm=None,
acktr=False):
self.actor_critic = actor_critic
self.acktr = acktr
self.value_loss_coef = value_loss_coef
self.entropy_coef = entropy_coef
self.max_grad_norm = max_grad_norm
if acktr:
self.optimizer = KFACOptimizer(actor_critic)
self.beta_actor_list = []
self.param_list = []
for name, param in actor_critic.named_parameters():
if "base.beta_net_actor" in name :
self.beta_actor_list.append(param)
else:
self.param_list.append(param)
else:
# Pierre: separate learning rates for beta net and actor net
self.optimizer = optim.RMSprop([{'params': self.param_list},
{'params': self.beta_actor_list, 'lr': lr_beta, 'weight_decay':reg_beta}], lr, eps=eps, alpha=alpha)
def __init__(self,
actor_critic,
value_loss_coef,
entropy_coef,
lr=None,
eps=None,
alpha=None,
max_grad_norm=None,
acktr=False,
path_recorder=None,
cost_evaluator=None,
arch_loss_coef=0):
self.actor_critic = actor_critic
self.acktr = acktr
self.value_loss_coef = value_loss_coef
self.entropy_coef = entropy_coef
self.max_grad_norm = max_grad_norm
self.path_recorder = path_recorder
self.cost_evaluator = cost_evaluator
self.arch_loss_coef = arch_loss_coef
if acktr:
self.optimizer = KFACOptimizer(actor_critic)
else:
self.optimizer = optim.RMSprop(actor_critic.parameters(),
lr,
eps=eps,
alpha=alpha)
def init_optimizer(self):
if self.acktr:
self.optimizer = KFACOptimizer(self.actor_critic)
else:
self.optimizer = optim.RMSprop(self.actor_critic.parameters(),
self.lr,
eps=self.eps,
alpha=self.alpha)
self.schedulers = ExponentialLR(self.optimizer, self.lr_decay)
def __init__(self,
actor_critic,
value_loss_coef,
entropy_coef,
lr=None,
filter_mem=None,
eps=None,
alpha=None,
max_grad_norm=None,
acktr=False):
self.actor_critic = actor_critic
self.acktr = acktr
self.value_loss_coef = value_loss_coef
self.entropy_coef = entropy_coef
self.max_grad_norm = max_grad_norm
if acktr:
self.optimizer = KFACOptimizer(actor_critic)
self.filter_list = []
self.param_list = []
for name, param in actor_critic.named_parameters():
if "base.filter_net" in name:
self.filter_list.append(param)
else:
self.param_list.append(param)
else:
self.optimizer = optim.RMSprop([{
'params': self.param_list
}, {
'params': self.filter_list
}],
lr,
eps=eps,
alpha=alpha)
def __init__(self,
actor_critic,
value_loss_coef,
entropy_coef,
lr=None,
lr_beta=None,
reg_beta=None,
delib_center=0.5,
eps=None,
alpha=None,
max_grad_norm=None,
acktr=False):
self.actor_critic = actor_critic
self.acktr = acktr
self.value_loss_coef = value_loss_coef
self.entropy_coef = entropy_coef
self.max_grad_norm = max_grad_norm
self.reg_beta = reg_beta
self.delib_center = delib_center
if acktr:
self.optimizer = KFACOptimizer(actor_critic)
self.beta_value_list = []
self.param_list = []
for name, param in actor_critic.named_parameters():
if "base.beta_value_net" in name :
self.beta_value_list.append(param)
else:
self.param_list.append(param)
else:
self.optimizer = optim.RMSprop([{'params': self.param_list},
{'params': self.beta_value_list, 'lr': lr_beta}], lr, eps=eps, alpha=alpha)
