repo_description: Fine-tuned  MARL algorithms on SMAC (100% win rates on most scenarios)
repo_topics: marl reinforcement-learning smac sota starcraft
code: # From https://github.com/wjh720/QPLEX/, added here for convenience.
  import copy
  from components.episode_buffer import EpisodeBatch
  from modules.mixers.dmaq_general import DMAQer
  import torch.nn.functional as F
  import torch as th
  from torch.optim import Adam
  import numpy as np
  from utils.rl_utils import build_td_lambda_targets
  from utils.th_utils import get_parameters_num
  
  class DMAQ_qattenLearner:
      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 == "dmaq":
                  self.mixer = DMAQer(args)
              else:
                  raise ValueError("Mixer {} not recognised.".format(args.mixer))
              self.params += list(self.mixer.parameters())
              self.target_mixer = copy.deepcopy(self.mixer)
  
          self.optimiser = Adam(params=self.params, lr=args.lr)
  
          print('Mixer Size: ')
          print(get_parameters_num(self.mixer.parameters()))
  
          # 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
  
          self.n_actions = self.args.n_actions
  
      def sub_train(self, batch: EpisodeBatch, t_env: int, episode_num: int, mac, mixer, optimiser, params,
                    show_demo=False, save_data=None):
          # 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"]
          actions_onehot = batch["actions_onehot"][:, :-1]
  
          # Calculate estimated Q-Values
          mac_out = []
          mac.init_hidden(batch.batch_size)
          for t in range(batch.max_seq_length):
              agent_outs = 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
  
          x_mac_out = mac_out.clone().detach()
          x_mac_out[avail_actions == 0] = -9999999
          max_action_qvals, max_action_index = x_mac_out[:, :-1].max(dim=3)
  
          max_action_index = max_action_index.detach().unsqueeze(3)
          is_max_action = (max_action_index == actions).int().float()
  
          if show_demo:
              q_i_data = chosen_action_qvals.detach().cpu().numpy()
              q_data = (max_action_qvals - chosen_action_qvals).detach().cpu().numpy()
              # self.logger.log_stat('agent_1_%d_q_1' % save_data[0], np.squeeze(q_data)[0], t_env)
              # self.logger.log_stat('agent_2_%d_q_2' % save_data[1], np.squeeze(q_data)[1], t_env)
  
          # 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, dim=1)  # Concat across time
  
          # Mask out unavailable actions
          target_mac_out[avail_actions == 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.max(dim=3, keepdim=True)[1]
              target_chosen_qvals = th.gather(target_mac_out, 3, cur_max_actions).squeeze(3)
              target_max_qvals = target_mac_out.max(dim=3)[0]
  
              cur_max_actions_onehot = th.zeros(cur_max_actions.squeeze(3).shape + (self.n_actions,)).cuda()
              cur_max_actions_onehot = cur_max_actions_onehot.scatter_(3, cur_max_actions, 1)
          else:
              raise "Use Double Q"
  
          # Mix
          if mixer is not None:
              ans_chosen = mixer(chosen_action_qvals, batch["state"][:, :-1], is_v=True)
              ans_adv = mixer(chosen_action_qvals, batch["state"][:, :-1], actions=actions_onehot,
                              max_q_i=max_action_qvals, is_v=False)
              chosen_action_qvals = ans_chosen + ans_adv
  
              if self.args.double_q:
                  target_chosen = self.target_mixer(target_chosen_qvals, batch["state"], is_v=True)
                  target_adv = self.target_mixer(target_chosen_qvals, batch["state"],
                                                  actions=cur_max_actions_onehot,
                                                  max_q_i=target_max_qvals, is_v=False)
                  target_max_qvals = target_chosen + target_adv
              else:
                  raise "Use Double Q"
  
          # Calculate 1-step Q-Learning targets
          targets = build_td_lambda_targets(rewards, terminated, mask, target_max_qvals, 
                                      self.args.n_agents, self.args.gamma, self.args.td_lambda)
  
          if show_demo:
              tot_q_data = chosen_action_qvals.detach().cpu().numpy()
              tot_target = targets.detach().cpu().numpy()
              print('action_pair_%d_%d' % (save_data[0], save_data[1]), np.squeeze(q_data[:, 0]),
                    np.squeeze(q_i_data[:, 0]), np.squeeze(tot_q_data[:, 0]), np.squeeze(tot_target[:, 0]))
              self.logger.log_stat('action_pair_%d_%d' % (save_data[0], save_data[1]),
                                   np.squeeze(tot_q_data[:, 0]), t_env)
              return
  
          # 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 = 0.5 * (masked_td_error ** 2).sum() / mask.sum()
  
          masked_hit_prob = th.mean(is_max_action, dim=2) * mask
          hit_prob = masked_hit_prob.sum() / mask.sum()
  
          # Optimise
          optimiser.zero_grad()
          loss.backward()
          grad_norm = th.nn.utils.clip_grad_norm_(params, self.args.grad_norm_clip)
          optimiser.step()
  
          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("hit_prob", hit_prob.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 train(self, batch: EpisodeBatch, t_env: int, episode_num: int, show_demo=False, save_data=None):
          self.sub_train(batch, t_env, episode_num, self.mac, self.mixer, self.optimiser, self.params,
                         show_demo=show_demo, save_data=save_data)
          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
  
      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.target_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))
  