text: import copy
  
  from components.episode_buffer import EpisodeBatch
  from modules.mixers.maven_qmix import MAVENMixer
  from modules.utils.maven_module import Discrim, RNNAggregator
  from utils.th_utils import get_parameters_num
  import torch as th
  from torch.optim import Adam
  
  import numpy as np
  
  
  class MAVENLearner:
      def __init__(self, mac, scheme, logger, args):
          self.args = args
          self.mac = mac
          self.logger = logger
  
          self.params = list(mac.parameters())
  
          self.last_target_update_episode = 0
  
          self.mixer = None
          if args.mixer is not None:
              if args.mixer == "maven_mix":
                  self.mixer = MAVENMixer(args)
              else:
                  raise ValueError("Mixer {} not recognised.".format(args.mixer))
              print('Mixer Size: ')
              print(get_parameters_num(self.mixer.parameters()))
              self.params += list(self.mixer.parameters())
              self.target_mixer = copy.deepcopy(self.mixer)
  
          # maven unique
          discrim_input = np.prod(self.args.state_shape) + self.args.n_agents * self.args.n_actions
  
          if self.args.rnn_discrim:
              self.rnn_agg = RNNAggregator(discrim_input, args)
              self.discrim = Discrim(args.rnn_agg_size, self.args.noise_dim, args)
              self.params += list(self.discrim.parameters())
              self.params += list(self.rnn_agg.parameters())
              if self.args.use_cuda:
                  self.rnn_agg = self.rnn_agg.cuda()
                  self.discrim = self.discrim.cuda()
          else:
              self.discrim = Discrim(discrim_input, self.args.noise_dim, args)
              self.params += list(self.discrim.parameters())
              if self.args.use_cuda:
                  self.discrim = self.discrim.cuda()
  
          self.discrim_loss = th.nn.CrossEntropyLoss(reduction="none")
  
          self.optimiser = Adam(params=self.params, lr=args.lr)
  
          # a little wasteful to deepcopy (e.g. duplicates action selector), but should work for any MAC
          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"]
          noise = batch["noise"][:, 0].unsqueeze(1).repeat(1,rewards.shape[1],1)
  
          # Calculate estimated Q-Values
          mac_out = []
          self.mac.init_hidden(batch.batch_size)
          for t in range(batch.max_seq_length):
              agent_outs = self.mac.forward(batch, t=t)
              mac_out.append(agent_outs)
          mac_out = th.stack(mac_out, dim=1)  # Concat over time
  
          # 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
  
          # 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_outs = self.target_mac.forward(batch, t=t)
              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_detach = mac_out.clone().detach()
              mac_out_detach[avail_actions == 0] = -9999999
              cur_max_actions = mac_out_detach[:, 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], noise)
              target_max_qvals = self.target_mixer(target_max_qvals, batch["state"][:, 1:], noise)
  
          # maven
          # mac_out[avail_actions == 0] = -9999999
          q_softmax_actions = th.nn.functional.softmax(mac_out[:, :-1], dim=3)
  
          if self.args.hard_qs:
              maxs = th.max(mac_out[:, :-1], dim=3, keepdim=True)[1]
              zeros = th.zeros_like(q_softmax_actions)
              zeros.scatter_(dim=3, index=maxs, value=1)
              q_softmax_actions = zeros
  
          q_softmax_agents = q_softmax_actions.reshape(q_softmax_actions.shape[0], q_softmax_actions.shape[1], -1)
  
          states = batch["state"][:, :-1]
          state_and_softactions = th.cat([q_softmax_agents, states], dim=2)
  
          if self.args.rnn_discrim:
              h_to_use = th.zeros(size=(batch.batch_size, self.args.rnn_agg_size)).to(states.device)
              hs = th.ones_like(h_to_use)
              for t in range(batch.max_seq_length - 1):
                  hs = self.rnn_agg(state_and_softactions[:, t], hs)
                  for b in range(batch.batch_size):
                      if t == batch.max_seq_length - 2 or (mask[b, t] == 1 and mask[b, t+1] == 0):
                          # This is the last timestep of the sequence
                          h_to_use[b] = hs[b]
              s_and_softa_reshaped = h_to_use
          else:
              s_and_softa_reshaped = state_and_softactions.reshape(-1, state_and_softactions.shape[-1])
  
          if self.args.mi_intrinsic:
              s_and_softa_reshaped = s_and_softa_reshaped.detach()
  
          discrim_prediction = self.discrim(s_and_softa_reshaped)
  
          # Cross-Entropy
          target_repeats = 1
          if not self.args.rnn_discrim:
              target_repeats = q_softmax_actions.shape[1]
          discrim_target = batch["noise"][:, 0].long().detach().max(dim=1)[1].unsqueeze(1).repeat(1, target_repeats).reshape(-1)
          discrim_loss = self.discrim_loss(discrim_prediction, discrim_target)
  
          if self.args.rnn_discrim:
              averaged_discrim_loss = discrim_loss.mean()
          else:
              masked_discrim_loss = discrim_loss * mask.reshape(-1)
              averaged_discrim_loss = masked_discrim_loss.sum() / mask.sum()
          self.logger.log_stat("discrim_loss", averaged_discrim_loss.item(), t_env)
  
  
          # Calculate 1-step Q-Learning targets
          targets = rewards + self.args.gamma * (1 - terminated) * target_max_qvals
          if self.args.mi_intrinsic:
              assert self.args.rnn_discrim is False
              targets = targets + self.args.mi_scaler * discrim_loss.view_as(rewards)
  
  
          # 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()
  
          loss = loss + self.args.mi_loss * averaged_discrim_loss
          
          # 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))