text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import torch.optim as optim
  from . import ARCLSR1q
  
  
  class PPO():
      def __init__(self,
                   actor_critic,
                   clip_param,
                   ppo_epoch,
                   num_mini_batch,
                   value_loss_coef,
                   entropy_coef,
                   lr=None,
                   eps=None,
                   max_grad_norm=None,
                   use_clipped_value_loss=True):
  
          self.actor_critic = actor_critic
  
          self.clip_param = clip_param
          self.ppo_epoch = ppo_epoch
          self.num_mini_batch = num_mini_batch
  
          self.value_loss_coef = value_loss_coef
          self.entropy_coef = entropy_coef
  
          self.max_grad_norm = max_grad_norm
          self.use_clipped_value_loss = use_clipped_value_loss
  
          #self.optimizer = optim.Adam(actor_critic.parameters(), lr = lr, eps = eps)
          self.optimizer = ARCLSR1q.ARCLSR1(actor_critic.parameters(),
                  gamma1=1, 
                  gamma2=10,
                  eta1=0.75,
                  eta2=0.95, 
                  history_size=15,
                  mu=1e8,
                  max_iters=15)
  
      def update(self, rollouts):
          advantages = rollouts.returns[:-1] - rollouts.value_preds[:-1]
          advantages = (advantages - advantages.mean()) / (
              advantages.std() + 1e-5)
  
          value_loss_epoch = 0
          action_loss_epoch = 0
          dist_entropy_epoch = 0
  
          for e in range(self.ppo_epoch):
              if self.actor_critic.is_recurrent:
                  data_generator = rollouts.recurrent_generator(
                      advantages, self.num_mini_batch)
              else:
                  data_generator = rollouts.feed_forward_generator(
                      advantages, self.num_mini_batch)
  
              for sample in data_generator:
                  obs_batch, recurrent_hidden_states_batch, actions_batch, \
                     value_preds_batch, return_batch, masks_batch, old_action_log_probs_batch, \
                          adv_targ = sample
  
                  # Reshape to do in a single forward pass for all steps
                  values, action_log_probs, dist_entropy, _ = self.actor_critic.evaluate_actions(
                      obs_batch, recurrent_hidden_states_batch, masks_batch,
                      actions_batch)
  
                  # LOSS FUNCTION PPO
                  ratio = torch.exp(action_log_probs -
                                    old_action_log_probs_batch)
                  surr1 = ratio * adv_targ
                  surr2 = torch.clamp(ratio, 1.0-self.clip_param, 1.0 + self.clip_param) * adv_targ
                  action_loss = -torch.min(surr1, surr2).mean()
  
                  if self.use_clipped_value_loss:
                      value_pred_clipped = value_preds_batch + \
                          (values - value_preds_batch).clamp(-self.clip_param, self.clip_param)
                      value_losses = (values - return_batch).pow(2)
                      value_losses_clipped = (
                          value_pred_clipped - return_batch).pow(2)
                      value_loss = 0.5 * torch.max(value_losses,
                                                   value_losses_clipped).mean()
                  else:
                      value_loss = 0.5 * (return_batch - values).pow(2).mean()
  
                  self.optimizer.zero_grad()
                  (value_loss * self.value_loss_coef + action_loss -
                   dist_entropy * self.entropy_coef).backward()
                  nn.utils.clip_grad_norm_(self.actor_critic.parameters(),
                                           self.max_grad_norm)
                  ###########################################################
                  def closure():
                      obs_batch, recurrent_hidden_states_batch, actions_batch, \
                     value_preds_batch, return_batch, masks_batch, old_action_log_probs_batch, \
                          adv_targ = sample
  
                      # Reshape to do in a single forward pass for all steps
                      values, action_log_probs, dist_entropy, _ = self.actor_critic.evaluate_actions(
                          obs_batch, recurrent_hidden_states_batch, masks_batch,
                          actions_batch)
  
                      # LOSS FUNCTION PPO
                      ratio = torch.exp(action_log_probs -
                                    old_action_log_probs_batch)
                      surr1 = ratio * adv_targ
                      surr2 = torch.clamp(ratio, 1.0-self.clip_param, 1.0 + self.clip_param) * adv_targ
                      action_loss = -torch.min(surr1, surr2).mean()
  
                      if self.use_clipped_value_loss:
                          value_pred_clipped = value_preds_batch + \
                              (values - value_preds_batch).clamp(-self.clip_param, self.clip_param)
                          value_losses = (values - return_batch).pow(2)
                          value_losses_clipped = (
                              value_pred_clipped - return_batch).pow(2)
                          value_loss = 0.5 * torch.max(value_losses,
                                                       value_losses_clipped).mean()
                      else:
                          value_loss = 0.5 * (return_batch - values).pow(2).mean()
  
                      return (value_loss * self.value_loss_coef + action_loss -
                       dist_entropy * self.entropy_coef)
  
                  ###############################################
  
                  self.optimizer.step(closure =closure)
  
                  value_loss_epoch += value_loss.item()
                  action_loss_epoch += action_loss.item()
                  dist_entropy_epoch += dist_entropy.item()
  
          num_updates = self.ppo_epoch * self.num_mini_batch
  
          value_loss_epoch /= num_updates
          action_loss_epoch /= num_updates
          dist_entropy_epoch /= num_updates
  
          return value_loss_epoch, action_loss_epoch, dist_entropy_epoch
  