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)
