text: from typing import Any, Dict, List, Optional, Tuple, Type, Union
  
  import numpy as np
  import torch as th
  from torch.nn import functional as F
  
  from stable_baselines3.common import logger
  from stable_baselines3.common.noise import ActionNoise
  from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm
  from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
  from stable_baselines3.common.utils import polyak_update
  from stable_baselines3.td3.policies import TD3Policy
  
  
  class TD3(OffPolicyAlgorithm):
      """
      Twin Delayed DDPG (TD3)
      Addressing Function Approximation Error in Actor-Critic Methods.
  
      Original implementation: https://github.com/sfujim/TD3
      Paper: https://arxiv.org/abs/1802.09477
      Introduction to TD3: https://spinningup.openai.com/en/latest/algorithms/td3.html
  
      :param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)
      :param env: The environment to learn from (if registered in Gym, can be str)
      :param learning_rate: learning rate for adam optimizer,
          the same learning rate will be used for all networks (Q-Values, Actor and Value function)
          it can be a function of the current progress remaining (from 1 to 0)
      :param buffer_size: size of the replay buffer
      :param learning_starts: how many steps of the model to collect transitions for before learning starts
      :param batch_size: Minibatch size for each gradient update
      :param tau: the soft update coefficient ("Polyak update", between 0 and 1)
      :param gamma: the discount factor
      :param train_freq: Update the model every ``train_freq`` steps. Set to `-1` to disable.
      :param gradient_steps: How many gradient steps to do after each rollout
          (see ``train_freq`` and ``n_episodes_rollout``)
          Set to ``-1`` means to do as many gradient steps as steps done in the environment
          during the rollout.
      :param n_episodes_rollout: Update the model every ``n_episodes_rollout`` episodes.
          Note that this cannot be used at the same time as ``train_freq``. Set to `-1` to disable.
      :param action_noise: the action noise type (None by default), this can help
          for hard exploration problem. Cf common.noise for the different action noise type.
      :param optimize_memory_usage: Enable a memory efficient variant of the replay buffer
          at a cost of more complexity.
          See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195
      :param policy_delay: Policy and target networks will only be updated once every policy_delay steps
          per training steps. The Q values will be updated policy_delay more often (update every training step).
      :param target_policy_noise: Standard deviation of Gaussian noise added to target policy
          (smoothing noise)
      :param target_noise_clip: Limit for absolute value of target policy smoothing noise.
      :param create_eval_env: Whether to create a second environment that will be
          used for evaluating the agent periodically. (Only available when passing string for the environment)
      :param policy_kwargs: additional arguments to be passed to the policy on creation
      :param verbose: the verbosity level: 0 no output, 1 info, 2 debug
      :param seed: Seed for the pseudo random generators
      :param device: Device (cpu, cuda, ...) on which the code should be run.
          Setting it to auto, the code will be run on the GPU if possible.
      :param _init_setup_model: Whether or not to build the network at the creation of the instance
      """
  
      def __init__(
          self,
          policy: Union[str, Type[TD3Policy]],
          env: Union[GymEnv, str],
          learning_rate: Union[float, Schedule] = 1e-3,
          buffer_size: int = int(1e6),
          learning_starts: int = 100,
          batch_size: int = 100,
          tau: float = 0.005,
          gamma: float = 0.99,
          train_freq: int = -1,
          gradient_steps: int = -1,
          n_episodes_rollout: int = 1,
          action_noise: Optional[ActionNoise] = None,
          optimize_memory_usage: bool = False,
          policy_delay: int = 2,
          target_policy_noise: float = 0.2,
          target_noise_clip: float = 0.5,
          tensorboard_log: Optional[str] = None,
          create_eval_env: bool = False,
          policy_kwargs: Dict[str, Any] = None,
          verbose: int = 0,
          seed: Optional[int] = None,
          device: Union[th.device, str] = "auto",
          _init_setup_model: bool = True,
      ):
  
          super(TD3, self).__init__(
              policy,
              env,
              TD3Policy,
              learning_rate,
              buffer_size,
              learning_starts,
              batch_size,
              tau,
              gamma,
              train_freq,
              gradient_steps,
              n_episodes_rollout,
              action_noise=action_noise,
              policy_kwargs=policy_kwargs,
              tensorboard_log=tensorboard_log,
              verbose=verbose,
              device=device,
              create_eval_env=create_eval_env,
              seed=seed,
              sde_support=False,
              optimize_memory_usage=optimize_memory_usage,
          )
  
          self.policy_delay = policy_delay
          self.target_noise_clip = target_noise_clip
          self.target_policy_noise = target_policy_noise
  
          if _init_setup_model:
              self._setup_model()
  
      def _setup_model(self) -> None:
          super(TD3, self)._setup_model()
          self._create_aliases()
  
      def _create_aliases(self) -> None:
          self.actor = self.policy.actor
          self.actor_target = self.policy.actor_target
          self.critic = self.policy.critic
          self.critic_target = self.policy.critic_target
  
      def train(self, gradient_steps: int, batch_size: int = 100) -> None:
  
          # Update learning rate according to lr schedule
          self._update_learning_rate([self.actor.optimizer, self.critic.optimizer])
  
          actor_losses, critic_losses = [], []
  
          for gradient_step in range(gradient_steps):
  
              # Sample replay buffer
              replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)
  
              with th.no_grad():
                  # Select action according to policy and add clipped noise
                  noise = replay_data.actions.clone().data.normal_(0, self.target_policy_noise)
                  noise = noise.clamp(-self.target_noise_clip, self.target_noise_clip)
                  next_actions = (self.actor_target(replay_data.next_observations) + noise).clamp(-1, 1)
  
                  # Compute the target Q value: min over all critics targets
                  targets = th.cat(self.critic_target(replay_data.next_observations, next_actions), dim=1)
                  target_q, _ = th.min(targets, dim=1, keepdim=True)
                  target_q = replay_data.rewards + (1 - replay_data.dones) * self.gamma * target_q
  
              # Get current Q estimates for each critic network
              current_q_estimates = self.critic(replay_data.observations, replay_data.actions)
  
              # Compute critic loss
              critic_loss = sum([F.mse_loss(current_q, target_q) for current_q in current_q_estimates])
              critic_losses.append(critic_loss.item())
  
              # Optimize the critics
              self.critic.optimizer.zero_grad()
              critic_loss.backward()
              self.critic.optimizer.step()
  
              # Delayed policy updates
              if gradient_step % self.policy_delay == 0:
                  # Compute actor loss
                  actor_loss = -self.critic.q1_forward(replay_data.observations, self.actor(replay_data.observations)).mean()
                  actor_losses.append(actor_loss.item())
  
                  # Optimize the actor
                  self.actor.optimizer.zero_grad()
                  actor_loss.backward()
                  self.actor.optimizer.step()
  
                  polyak_update(self.critic.parameters(), self.critic_target.parameters(), self.tau)
                  polyak_update(self.actor.parameters(), self.actor_target.parameters(), self.tau)
  
          self._n_updates += gradient_steps
          logger.record("train/n_updates", self._n_updates, exclude="tensorboard")
          logger.record("train/actor_loss", np.mean(actor_losses))
          logger.record("train/critic_loss", np.mean(critic_losses))
  
      def learn(
          self,
          total_timesteps: int,
          callback: MaybeCallback = None,
          log_interval: int = 4,
          eval_env: Optional[GymEnv] = None,
          eval_freq: int = -1,
          n_eval_episodes: int = 5,
          tb_log_name: str = "TD3",
          eval_log_path: Optional[str] = None,
          reset_num_timesteps: bool = True,
      ) -> OffPolicyAlgorithm:
  
          return super(TD3, self).learn(
              total_timesteps=total_timesteps,
              callback=callback,
              log_interval=log_interval,
              eval_env=eval_env,
              eval_freq=eval_freq,
              n_eval_episodes=n_eval_episodes,
              tb_log_name=tb_log_name,
              eval_log_path=eval_log_path,
              reset_num_timesteps=reset_num_timesteps,
          )
  
      def _excluded_save_params(self) -> List[str]:
          return super(TD3, self)._excluded_save_params() + ["actor", "critic", "actor_target", "critic_target"]
  
      def _get_torch_save_params(self) -> Tuple[List[str], List[str]]:
          state_dicts = ["policy", "actor.optimizer", "critic.optimizer"]
          return state_dicts, []
  