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"]) }