code: import copy
  from components.episode_buffer import EpisodeBatch
  from modules.mixers.vdn import VDNMixer
  from modules.mixers.qmix import QMixer
  import torch as th
  import numpy as np
  from torch.optim import RMSprop
  
  # learning for 6h_vs_8z scenario
  class QLearner_6h_vs_8z:
      def __init__(self, mac, scheme, logger, args):
          self.args = args
          self.mac = mac
          self.logger = logger
          self.regularization_const = self.args.normalization_const
          self.params = list(mac.parameters())
  
          self.last_target_update_episode = 0
  
          self.mixer = None
          if args.mixer is not None:
              if args.mixer == "vdn":
                  self.mixer = VDNMixer()
              elif args.mixer == "qmix":
                  self.mixer = QMixer(args)
              else:
                  raise ValueError("Mixer {} not recognised.".format(args.mixer))
              self.params += list(self.mixer.parameters())
              self.target_mixer = copy.deepcopy(self.mixer)
  
          self.params += list(self.mac.env_blender.parameters())
          self.optimiser = RMSprop(params=self.params, lr=args.lr, alpha=args.optim_alpha, eps=args.optim_eps)
          self.target_mac = copy.deepcopy(mac)
  
          self.log_stats_t = -self.args.learner_log_interval - 1
  
      def train(self, batch: EpisodeBatch, t_env: int, episode_num: int):
          # Get the relevant quantities
          rewards = batch["reward"][:, :-1]
          actions = batch["actions"][:, :-1]
          terminated = batch["terminated"][:, :-1].float()
          mask = batch["filled"][:, :-1].float()
          mask[:, 1:] = mask[:, 1:] * (1 - terminated[:, :-1])
          avail_actions = batch["avail_actions"]
  
          # Calculate estimated Q-Values
          mac_out = []
          difference_out = []
          difference_out1 = []         
          self.mac.init_hidden(batch.batch_size)
          for t in range(batch.max_seq_length):
              agent_local_outputs, hidden_states = self.mac.forward(batch, t=t)  
              dummy0 = self.mac.env_blender(hidden_states[:,0,:].view(32,-1)) 
              dummy1 = self.mac.env_blender(hidden_states[:,1,:].view(32,-1)) 
              dummy2 = self.mac.env_blender(hidden_states[:,2,:].view(32,-1)) 
              dummy3 = self.mac.env_blender(hidden_states[:,3,:].view(32,-1)) 
              dummy4 = self.mac.env_blender(hidden_states[:,4,:].view(32,-1))
              dummy5 = self.mac.env_blender(hidden_states[:,5,:].view(32,-1)) 
              
              agent0 = (dummy1 + dummy2 + dummy3 + dummy4 + dummy5)/5.0
              agent1 = (dummy0 + dummy2 + dummy3 + dummy4 + dummy5)/5.0
              agent2 = (dummy0 + dummy1 + dummy3 + dummy4 + dummy5)/5.0
              agent3 = (dummy0 + dummy1 + dummy2 + dummy4 + dummy5)/5.0
              agent4 = (dummy0 + dummy1 + dummy2 + dummy3 + dummy5)/5.0
              agent5 = (dummy0 + dummy1 + dummy2 + dummy3 + dummy4)/5.0
              agent_global_outputs =th.cat((agent0.view((32,1,14)),agent1.view((32,1,14)),agent2.view((32,1,14)),agent3.view((32,1,14)),agent4.view((32,1,14)),agent5.view((32,1,14))),1)            
              agent_outs = agent_local_outputs + agent_global_outputs
              difference = agent_global_outputs 
              mac_out.append(agent_outs)
              difference_out.append(difference)
              
          mac_out = th.stack(mac_out, dim=1)  # Concat over time
          difference_out = th.stack(difference_out, dim=1)  # Concat over time
          difference_out = th.std(difference_out,dim = 3).sum()
          # Pick the Q-Values for the actions taken by each agent
          chosen_action_qvals = th.gather(mac_out[:, :-1], dim=3, index=actions).squeeze(3)  # Remove the last dim
          avg_difference = (difference_out.sum())/((agent_outs.shape[0]*agent_outs.shape[1]*agent_outs.shape[2]*batch.max_seq_length))
          # Calculate the Q-Values necessary for the target
          target_mac_out = []
          
          self.target_mac.init_hidden(batch.batch_size)
          for t in range(batch.max_seq_length):
              target_agent_local_outputs, target_hidden_states = self.target_mac.forward(batch, t=t)
      
              dummy0 = self.mac.env_blender(target_hidden_states[:,0,:].view(32,-1)) 
              dummy1 = self.mac.env_blender(target_hidden_states[:,1,:].view(32,-1)) 
              dummy2 = self.mac.env_blender(target_hidden_states[:,2,:].view(32,-1)) 
              dummy3 = self.mac.env_blender(target_hidden_states[:,3,:].view(32,-1)) 
              dummy4 = self.mac.env_blender(target_hidden_states[:,4,:].view(32,-1))
              dummy5 = self.mac.env_blender(target_hidden_states[:,5,:].view(32,-1)) 
              
              target_agent0 = (dummy1 + dummy2 + dummy3 + dummy4 + dummy5)/5.0
              target_agent1 = (dummy0 + dummy2 + dummy3 + dummy4 + dummy5)/5.0
              target_agent2 = (dummy0 + dummy1 + dummy3 + dummy4 + dummy5)/5.0
              target_agent3 = (dummy0 + dummy1 + dummy2 + dummy4 + dummy5)/5.0
              target_agent4 = (dummy0 + dummy1 + dummy2 + dummy3 + dummy5)/5.0
              target_agent5 = (dummy0 + dummy1 + dummy2 + dummy3 + dummy4)/5.0  
              
              target_agent_global_outputs = th.cat((target_agent0.view((32,1,14)),target_agent1.view((32,1,14)),target_agent2.view((32,1,14)),target_agent3.view((32,1,14)),target_agent4.view((32,1,14)),target_agent5.view((32,1,14))),1)
              target_agent_outs = target_agent_local_outputs + target_agent_global_outputs
              target_mac_out.append(target_agent_outs)
            
          # We don't need the first timesteps Q-Value estimate for calculating targets
          target_mac_out = th.stack(target_mac_out[1:], dim=1)  # Concat across time
  
          # Mask out unavailable actions
          target_mac_out[avail_actions[:, 1:] == 0] = -9999999
  
          # Max over target Q-Values
          if self.args.double_q:
              # Get actions that maximise live Q (for double q-learning)
              mac_out[avail_actions == 0] = -9999999
              cur_max_actions = mac_out[:, 1:].max(dim=3, keepdim=True)[1]
              target_max_qvals = th.gather(target_mac_out, 3, cur_max_actions).squeeze(3)
          else:
              target_max_qvals = target_mac_out.max(dim=3)[0]
  
          # Mix
          if self.mixer is not None:
              chosen_action_qvals = self.mixer(chosen_action_qvals, batch["state"][:, :-1])
              target_max_qvals = self.target_mixer(target_max_qvals, batch["state"][:, 1:])
  
          # Calculate 1-step Q-Learning targets
          targets = rewards + self.args.gamma * (1 - terminated) * target_max_qvals
  
          # Td-error
          td_error = (chosen_action_qvals - targets.detach())
  
          mask = mask.expand_as(td_error)
  
          # 0-out the targets that came from padded data
          masked_td_error = td_error * mask
  
          # Normal L2 loss, take mean over actual data
          loss = (masked_td_error ** 2).sum() / mask.sum() + self.args.normalization_const * avg_difference
  
          # Optimise
          self.optimiser.zero_grad()
          loss.backward()
          grad_norm = th.nn.utils.clip_grad_norm_(self.params, self.args.grad_norm_clip)
          self.optimiser.step()
  
          if (episode_num - self.last_target_update_episode) / self.args.target_update_interval >= 1.0:
              self._update_targets()
              self.last_target_update_episode = episode_num
  
          if t_env - self.log_stats_t >= self.args.learner_log_interval:
              self.logger.log_stat("loss", loss.item(), t_env)
              self.logger.log_stat("grad_norm", grad_norm, t_env)
              mask_elems = mask.sum().item()
              self.logger.log_stat("td_error_abs", (masked_td_error.abs().sum().item()/mask_elems), t_env)
              self.logger.log_stat("q_taken_mean", (chosen_action_qvals * mask).sum().item()/(mask_elems * self.args.n_agents), t_env)
              self.logger.log_stat("target_mean", (targets * mask).sum().item()/(mask_elems * self.args.n_agents), t_env)
              self.log_stats_t = t_env
  
      def _update_targets(self):
          self.target_mac.load_state(self.mac)
          if self.mixer is not None:
              self.target_mixer.load_state_dict(self.mixer.state_dict())
          self.logger.console_logger.info("Updated target network")
  
      def cuda(self):
          self.mac.cuda()
          self.target_mac.cuda()
          if self.mixer is not None:
              self.mixer.cuda()
              self.target_mixer.cuda()
  
      def save_models(self, path):
          self.mac.save_models(path)
          if self.mixer is not None:
              th.save(self.mixer.state_dict(), "{}/mixer.th".format(path))
          th.save(self.optimiser.state_dict(), "{}/opt.th".format(path))
  
      def load_models(self, path):
          self.mac.load_models(path)
          # Not quite right but I don't want to save target networks
          self.target_mac.load_models(path)
          if self.mixer is not None:
              self.mixer.load_state_dict(th.load("{}/mixer.th".format(path), map_location=lambda storage, loc: storage))
          self.optimiser.load_state_dict(th.load("{}/opt.th".format(path), map_location=lambda storage, loc: storage))
  
  
  
  
apis: torch.stack torch.nn.utils.clip_grad_norm_ torch.std torch.gather torch.optim.RMSprop