text: """Implements base actor-critic agents."""
  
  import os
  import time
  
  from typing import Any, Dict
  
  import numpy as np
  
  from agents.base.agent import Agent
  from agents.base.policy_agents import PolicyAgent
  from agents.base.value_agents import ValueAgent
  from gym_ext.envs import Env
  
  
  class ActorCriticAgent(Agent):
      """Base class for actor-critic agents."""
  
      name = ""
  
      def set_actor_critic(self, actor: PolicyAgent, critic: ValueAgent):
          """
          Set the actor and critic.
  
          Args:
              actor (PolicyAgent): The actor.
              critic (ValueAgent): The critic.
          """
          self.actor = actor
          self.critic = critic
  
      def get_action(self, state: Any) -> Any:
          """
          Get the action to take.
  
          Args:
              state (Any): The state to get the action for.
  
          Returns:
              Any: The action to take.
          """
          return self.actor.get_action(state)
  
      def train(self, num_episodes: int = 10000, **kwargs):
          """
          Train the agent.
  
          Args:
              num_episodes (int): Number of episodes to train for.
              **kwargs: Additional arguments.
          """
          start = time.time()
          df = kwargs.get("discount_factor", 1.0)
          self.actor.policy.init_vars(self.n_features, self.n_actions)
          self.critic.model.init_vars(self.n_features, self.n_actions)
          steps = []
          last_info = 0
          for i in range(num_episodes):
              st = time.time()
              state = self.env.reset()
              action = self.actor.get_action(state)
              alpha = 0.0
              self.critic.reset_vars()
              updates = []
              while True:
                  alpha += 1.0
                  qvals = self.critic.get_qvals(state)
                  qval = qvals[action]
                  # print("+" * 80)
                  # print(self.critic.model.w)
                  # print("+" * 80)
                  # print(state)
                  # print("+" * 80)
                  # print(qvals)
                  # print("+" * 80)
                  state_, reward, done, info = self.env.step(action)
                  r = reward
                  if done:
                      if alpha > 170:
                          r = 5
                      else:
                          r = -10
                  action_ = self.actor.get_action(state_)
                  nqvals = self.critic.get_qvals(state_)
                  nqval = nqvals[action_]
                  agrad = self.actor.policy.grad(state, action)
                  updates.append(agrad * qval / alpha)
                  cgrad = self.critic.model.grad(state, action)
                  update = self.critic.algorithm.get_update(
                      r, qval, nqval, cgrad, self.critic.vec_shape, df)
                  self.critic.update_model(update  / alpha)
                  state, action = state_, action_
                  if done:
                      break
              for update in updates:
                  # print(update)
                  self.actor.policy.update_policy(update)
              steps.append(alpha)
              if (i + 1) % self.info_episode == 0:
                  msg = (f"Finished episode {i} in "
                         f"{int((time.time() - st) * 100000) / 100}ms.")
                  mx = max(steps)
                  avg = int(100 * sum(steps) / len(steps)) / 100
                  last = steps[last_info:]
                  mx_since_last = max(last)
                  avg_since_last = int(100 * sum(last) / len(last)) / 100
                  print(f"Max steps so far = {mx}..."
                        f"Average steps so far = {avg}")
                  print(f"Max steps since last info = {mx_since_last}..."
                        f"Average steps since last info = {avg_since_last}")
                  print(msg)
                  last_info = i
          print(f"Model trained in {int((time.time() - start) * 100) / 100}sec.")
  
      def update_metadata(self, metadata: Dict[str, Any]):
          """
          Update the metadata.
  
          Args:
              metadata (Dict[str, Any]): The metadata to update.
          """
          metadata = super(ActorCriticAgent, self).update_metadata(metadata)
          model_dir = metadata.get("model_dir")
          if not os.path.isdir(model_dir):
              raise IOError(f"Model directory {model_dir} not found")
          actor_model_dir = os.path.join(model_dir, "actor")
          if not os.path.isdir(actor_model_dir):
              os.makedirs(actor_model_dir)
          critic_model_dir = os.path.join(model_dir, "critic")
          if not os.path.isdir(critic_model_dir):
              os.makedirs(critic_model_dir)
          metadata["agent"]["actor_meta"] = self.actor.update_metadata(
              {"model_dir": actor_model_dir})
          metadata["agent"]["critic_meta"] = self.critic.update_metadata(
              {"model_dir": critic_model_dir})
          return metadata
  
      @classmethod
      def load_from_meta(cls, metadata: Dict[str, Any], env: Env
                         ) -> "ActorCriticAgent":
          """
          Load the agent from metadata.
  
          Args:
              metadata (Dict[str, Any]): The metadata to load from.
              env (Env): The environment to use.
  
          Returns:
              Agent: The loaded agent.
          """
          # agent = cls(env, metadata["policy"]["name"],
          #             verbose=metadata["verbose"])
          # agent.policy.deserialize(metadata["policy"])
          return cls(env)