repo_description: Fine-tuned  MARL algorithms on SMAC (100% win rates on most scenarios)
repo_topics: marl reinforcement-learning smac sota starcraft
code: import copy
  from components.episode_buffer import EpisodeBatch
  from modules.mixers.vdn import VDNMixer
  from modules.mixers.qmix import QMixer
  from utils.rl_utils import build_td_lambda_targets
  from components.action_selectors import categorical_entropy
  import torch as th
  from torch.optim import Adam, RMSprop
  
  """
  IAC and VDNs, this class should use ppo agents and ppo mac
  """
  
  class PGLearner_v2:
      def __init__(self, mac, scheme, logger, args):
          self.args = args
          self.n_agents = args.n_agents
          self.n_actions = args.n_actions
          self.mac = mac
          self.logger = logger
  
          self.last_target_update_step = 0
          self.critic_training_steps = 0
  
          self.log_stats_t = -self.args.learner_log_interval - 1
  
          self.target_mac = copy.deepcopy(mac)
          self.params = list(self.mac.parameters())
  
          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())
  
          if self.args.optim == 'adam':
              self.optimiser = Adam(params=self.params, lr=args.lr)
          else:
              self.optimiser = RMSprop(params=self.params, lr=args.lr)
  
      def train(self, batch: EpisodeBatch, t_env: int, episode_num: int):
          # Get the relevant quantities
          bs = batch.batch_size
          max_t = batch.max_seq_length
          rewards = batch["reward"][:, :-1]
          actions = batch["actions"][:, :]
          terminated = batch["terminated"][:, :-1].float()
          mask = batch["filled"][:, :-1].float()
          mask[:, 1:] = mask[:, 1:] * (1 - terminated[:, :-1])
          avail_actions = batch["avail_actions"][:, :]
  
          critic_mask = mask.clone()
          mask = mask.repeat(1, 1, self.n_agents).view(-1)
  
          advantages, td_error, targets_taken, log_pi_taken, entropy = self._calculate_advs(batch, rewards, terminated, actions, avail_actions,
                                                          critic_mask, bs, max_t)
  
          pg_loss = - ((advantages.detach() * log_pi_taken) * mask).sum() / mask.sum()
          vf_loss = ((td_error ** 2) * mask).sum() / mask.sum()
          entropy[mask == 0] = 0
          entropy_loss = (entropy * mask).sum() / mask.sum()
  
          coma_loss = pg_loss + self.args.vf_coef * vf_loss
          if self.args.ent_coef:
              coma_loss -= self.args.ent_coef * entropy_loss
  
          # Optimise agents
          self.optimiser.zero_grad()
          coma_loss.backward()
          grad_norm = th.nn.utils.clip_grad_norm_(self.params, self.args.grad_norm_clip)
          self.optimiser.step()
  
  
          if t_env - self.log_stats_t >= self.args.learner_log_interval:
              self.logger.log_stat("critic_loss", ((td_error ** 2) * mask).sum().item() / mask.sum().item(), t_env)
              self.logger.log_stat("td_error_abs", (td_error.abs() * mask).sum().item() / mask.sum().item(), t_env)
              self.logger.log_stat("q_taken_mean", (targets_taken * mask).sum().item() / mask.sum().item(), t_env)
              self.logger.log_stat("target_mean", ((targets_taken + advantages) * mask).sum().item() / mask.sum().item(), t_env)
              self.logger.log_stat("pg_loss", - ((advantages.detach() * log_pi_taken) * mask).sum().item() / mask.sum().item(), t_env)
              self.logger.log_stat("advantage_mean", (advantages * mask).sum().item() / mask.sum().item(), t_env)
              self.logger.log_stat("coma_loss", coma_loss.item(), t_env)
              self.logger.log_stat("entropy_loss", entropy_loss.item(), t_env)
              self.logger.log_stat("agent_grad_norm", grad_norm, t_env)
              # self.logger.log_stat("pi_max", (pi.max(dim=1)[0] * mask).sum().item() / mask.sum().item(), t_env)
              self.log_stats_t = t_env
  
      def _calculate_advs(self, batch, rewards, terminated, actions, avail_actions, mask, bs, max_t):
          mac_out = []
          q_outs = []
          # Roll out experiences
          self.mac.init_hidden(batch.batch_size)
          for t in range(batch.max_seq_length):
              agent_out, q_out = self.mac.forward(batch, t=t)
              mac_out.append(agent_out)
              q_outs.append(q_out)
          mac_out = th.stack(mac_out, dim=1)  # Concat over time
          q_outs = th.stack(q_outs, dim=1)  # Concat over time
  
          # Mask out unavailable actions, renormalise (as in action selection)
          # mac_out[avail_actions == 0] = 0
          # mac_out = mac_out/(mac_out.sum(dim=-1, keepdim=True) + 1e-5)
  
          # Calculated baseline
          pi = mac_out[:, :-1]  #[bs, t, n_agents, n_actions]
          pi_taken = th.gather(pi, dim=-1, index=actions[:, :-1]).squeeze(-1)    #[bs, t, n_agents]
          action_mask = mask.repeat(1, 1, self.n_agents)
          pi_taken[action_mask == 0] = 1.0
          log_pi_taken = th.log(pi_taken).reshape(-1)
  
          # Calculate entropy
          entropy = categorical_entropy(pi).reshape(-1)  #[bs, t, n_agents, 1]
  
          # Calculate q targets
          targets_taken = q_outs.squeeze(-1)   #[bs, t, n_agents]
          if self.args.mixer:
              targets_taken = self.mixer(targets_taken, batch["state"][:, :]) #[bs, t, 1]
  
          # Calculate td-lambda targets
          targets = build_td_lambda_targets(rewards, terminated, mask, targets_taken, self.n_agents, self.args.gamma, self.args.td_lambda)
  
          advantages = targets - targets_taken[:, :-1]
          advantages = advantages.unsqueeze(2).repeat(1, 1, self.n_agents, 1).reshape(-1)
  
          td_error = targets_taken[:, :-1] - targets.detach()
          td_error = td_error.unsqueeze(2).repeat(1, 1, self.n_agents, 1).reshape(-1)
  
  
          return advantages, td_error, targets_taken[:, :-1].unsqueeze(2).repeat(1, 1, self.n_agents, 1).reshape(-1), log_pi_taken, entropy
  
  
      def cuda(self):
          self.mac.cuda()
          if self.args.mixer:
              self.mixer.cuda()
  
      def save_models(self, path):
          self.mac.save_models(path)
          if self.args.mixer:
              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)
          if self.args.mixer:
              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))