text:     
  import time
  import wandb
  import os
  import numpy as np
  from itertools import chain
  import torch
  from tensorboardX import SummaryWriter
  
  from onpolicy.utils.separated_buffer import SeparatedReplayBuffer
  from onpolicy.utils.util import update_linear_schedule
  
  def _t2n(x):
      return x.detach().cpu().numpy()
  
  class Runner(object):
      def __init__(self, config):
  
          self.all_args = config['all_args']
          self.envs = config['envs']
          self.eval_envs = config['eval_envs']
          self.device = config['device']
          self.num_agents = config['num_agents']
  
          # parameters
          self.env_name = self.all_args.env_name
          self.algorithm_name = self.all_args.algorithm_name
          self.experiment_name = self.all_args.experiment_name
          self.use_centralized_V = self.all_args.use_centralized_V
          self.use_obs_instead_of_state = self.all_args.use_obs_instead_of_state
          self.num_env_steps = self.all_args.num_env_steps
          self.episode_length = self.all_args.episode_length
          self.n_rollout_threads = self.all_args.n_rollout_threads
          self.n_eval_rollout_threads = self.all_args.n_eval_rollout_threads
          self.use_linear_lr_decay = self.all_args.use_linear_lr_decay
          self.hidden_size = self.all_args.hidden_size
          self.use_wandb = self.all_args.use_wandb
          self.use_render = self.all_args.use_render
          self.recurrent_N = self.all_args.recurrent_N
  
          # interval
          self.save_interval = self.all_args.save_interval
          self.use_eval = self.all_args.use_eval
          self.eval_interval = self.all_args.eval_interval
          self.log_interval = self.all_args.log_interval
  
          # dir
          self.model_dir = self.all_args.model_dir
  
          if self.use_render:
              import imageio
              self.run_dir = config["run_dir"]
              self.gif_dir = str(self.run_dir / 'gifs')
              if not os.path.exists(self.gif_dir):
                  os.makedirs(self.gif_dir)
          else:
              if self.use_wandb:
                  self.save_dir = str(wandb.run.dir)
              else:
                  self.run_dir = config["run_dir"]
                  self.log_dir = str(self.run_dir / 'logs')
                  if not os.path.exists(self.log_dir):
                      os.makedirs(self.log_dir)
                  self.writter = SummaryWriter(self.log_dir)
                  self.save_dir = str(self.run_dir / 'models')
                  if not os.path.exists(self.save_dir):
                      os.makedirs(self.save_dir)
  
  
          from onpolicy.algorithms.r_mappo.r_mappo import R_MAPPO as TrainAlgo
          from onpolicy.algorithms.r_mappo.algorithm.rMAPPOPolicy import R_MAPPOPolicy as Policy
  
  
          self.policy = []
          for agent_id in range(self.num_agents):
              share_observation_space = self.envs.share_observation_space[agent_id] if self.use_centralized_V else self.envs.observation_space[agent_id]
              # policy network
              po = Policy(self.all_args,
                          self.envs.observation_space[agent_id],
                          share_observation_space,
                          self.envs.action_space[agent_id],
                          device = self.device)
              self.policy.append(po)
  
          if self.model_dir is not None:
              self.restore()
  
          self.trainer = []
          self.buffer = []
          for agent_id in range(self.num_agents):
              # algorithm
              tr = TrainAlgo(self.all_args, self.policy[agent_id], device = self.device)
              # buffer
              share_observation_space = self.envs.share_observation_space[agent_id] if self.use_centralized_V else self.envs.observation_space[agent_id]
              bu = SeparatedReplayBuffer(self.all_args,
                                         self.envs.observation_space[agent_id],
                                         share_observation_space,
                                         self.envs.action_space[agent_id])
              self.buffer.append(bu)
              self.trainer.append(tr)
              
      def run(self):
          raise NotImplementedError
  
      def warmup(self):
          raise NotImplementedError
  
      def collect(self, step):
          raise NotImplementedError
  
      def insert(self, data):
          raise NotImplementedError
      
      @torch.no_grad()
      def compute(self):
          for agent_id in range(self.num_agents):
              self.trainer[agent_id].prep_rollout()
              next_value = self.trainer[agent_id].policy.get_values(self.buffer[agent_id].share_obs[-1], 
                                                                  self.buffer[agent_id].rnn_states_critic[-1],
                                                                  self.buffer[agent_id].masks[-1])
              next_value = _t2n(next_value)
              self.buffer[agent_id].compute_returns(next_value, self.trainer[agent_id].value_normalizer)
  
      def train(self):
          train_infos = []
          for agent_id in range(self.num_agents):
              self.trainer[agent_id].prep_training()
              train_info = self.trainer[agent_id].train(self.buffer[agent_id])
              train_infos.append(train_info)       
              self.buffer[agent_id].after_update()
  
          return train_infos
  
      def save(self):
          for agent_id in range(self.num_agents):
              policy_actor = self.trainer[agent_id].policy.actor
              #torch.save(policy_actor.state_dict(), str(self.save_dir) + "/actor_agent" + str(agent_id) + ".pt")
              torch.save(policy_actor.state_dict(), str(self.save_dir) + "/actor" + ".pt")
              policy_critic = self.trainer[agent_id].policy.critic
              #torch.save(policy_critic.state_dict(), str(self.save_dir) + "/critic_agent" + str(agent_id) + ".pt")
              torch.save(policy_critic.state_dict(), str(self.save_dir) + "/critic" + ".pt")
  
      def restore(self):
          for agent_id in range(self.num_agents):
              #policy_actor_state_dict = torch.load(str(self.model_dir) + '/actor_agent' + str(agent_id) + '.pt')
              policy_actor_state_dict = torch.load(str(self.model_dir) + '/actor' + '.pt')
              self.policy[agent_id].actor.load_state_dict(policy_actor_state_dict)
              #policy_critic_state_dict = torch.load(str(self.model_dir) + '/critic_agent' + str(agent_id) + '.pt')
              policy_critic_state_dict = torch.load(str(self.model_dir) + '/critic' + '.pt')
              self.policy[agent_id].critic.load_state_dict(policy_critic_state_dict)
  
      def log_train(self, train_infos, total_num_steps): 
          for agent_id in range(self.num_agents):
              for k, v in train_infos[agent_id].items():
                  agent_k = "agent%i/" % agent_id + k
                  if self.use_wandb:
                      wandb.log({agent_k: v}, step=total_num_steps)
                  else:
                      self.writter.add_scalars(agent_k, {agent_k: v}, total_num_steps)
  
      def log_env(self, env_infos, total_num_steps):
          for k, v in env_infos.items():
              if len(v) > 0:
                  if self.use_wandb:
                      wandb.log({k: np.mean(v)}, step=total_num_steps)
                  else:
                      self.writter.add_scalars(k, {k: np.mean(v)}, total_num_steps)
  