def save_params(self, n_episode: int): """Save model and optimizer parameters.""" params = { "actor_state_dict": self.actor.state_dict(), "critic_state_dict": self.critic.state_dict(), "actor_optim_state_dict": self.actor_optimizer.state_dict(), "critic_optim_state_dict": self.critic_optimizer.state_dict(), } Agent.save_params(self, params, n_episode)
def save_params(self, n_episode: int): """Save model and optimizer parameters.""" params = { "dqn_state_dict": self.dqn.state_dict(), "dqn_target_state_dict": self.dqn_target.state_dict(), "dqn_optim_state_dict": self.dqn_optimizer.state_dict(), } Agent.save_params(self, params, n_episode)
def save_params(self, n_episode: int): """Save model and optimizer parameters.""" params = { "actor": self.actor.state_dict(), "actor_target": self.actor_target.state_dict(), "actor_optim": self.actor_optim.state_dict(), "critic1": self.critic1.state_dict(), "critic2": self.critic2.state_dict(), "critic_target1": self.critic_target1.state_dict(), "critic_target2": self.critic_target2.state_dict(), "critic_optim": self.critic_optim.state_dict(), } Agent.save_params(self, params, n_episode)
def save_params(self, n_episode: int): """Save model and optimizer parameters.""" params = { "actor": self.actor.state_dict(), "qf_1": self.qf_1.state_dict(), "qf_2": self.qf_2.state_dict(), "vf": self.vf.state_dict(), "vf_target": self.vf_target.state_dict(), "actor_optim": self.actor_optimizer.state_dict(), "qf_1_optim": self.qf_1_optimizer.state_dict(), "qf_2_optim": self.qf_2_optimizer.state_dict(), "vf_optim": self.vf_optimizer.state_dict(), } if self.hyper_params["AUTO_ENTROPY_TUNING"]: params["alpha_optim"] = self.alpha_optimizer.state_dict() Agent.save_params(self, params, n_episode)