def make_optimizers(self):
config = dutil.deep_merge(
DEFAULT_OPTIM_CONFIG,
self.config["torch_optimizer"],
False,
[],
["actor", "critic"],
)
assert config["actor"]["type"] in [
"KFAC",
"EKFAC",
], "ACKTR must use optimizer with Kronecker Factored curvature estimation."
return {
"actor": build_optimizer(self.module.actor, config["actor"]),
"critic": build_optimizer(self.module.critic, config["critic"]),
}
def make_optimizers(self):
config = self.config["torch_optimizer"]
components = "models actor critics alpha".split()
return {
name: build_optimizer(self.module[name], config[name])
for name in components
}
def make_optimizers(self):
config = self.config["torch_optimizer"]
components = "model actor critics".split()
if self.config["true_model"]:
components = components[1:]
return {
name: build_optimizer(self.module[name], config[name])
for name in components
}
def make_optimizers(self):
config = self.config["torch_optimizer"]
components = {
"model": self.module.model,
"actor": self.module.actor,
"critic": self.module.critic,
"alpha": self.module.alpha,
}
return {
name: build_optimizer(module, config[name])
for name, module in components.items()
}
def make_optimizers(self):
"""PyTorch optimizers to use."""
config = self.config["torch_optimizer"]
component_map = {
"on_policy": self.module.actor,
"off_policy":
nn.ModuleList([self.module.model, self.module.critic]),
}
return {
name: build_optimizer(module, config[name])
for name, module in component_map.items()
}
def make_optimizers(self):
return {
"models": build_optimizer(self.module.models, {"type": "Adam"})
}
def make_optimizers(self):
return {
"critic":
build_optimizer(self.module.critic,
self.config["critic_optimizer"])
}
def make_optimizers(self):
return {
"naf":
build_optimizer(self.module.critics,
self.config["torch_optimizer"])
}
