docstr: Creates a simple vectorized wrapper for multiple environments
  
  Args:
      scenario_name (str): scenario of MultiAgentEnv
      env_num (int): number of parallel envs to train
      seed (int): random seed
docstr_tok: ["Creates","a","simple","vectorized","wrapper","for","multiple","environments","Args",":","scenario_name","(","str",")",":","scenario","of","MultiAgentEnv","env_num","(","int",")",":","number","of","parallel","envs","to","train","seed","(","int",")",":","random","seed"]
code: def __init__(self, scenario_name, env_num, seed):
      """
      Creates a simple vectorized wrapper for multiple environments
  
      Args:
          scenario_name (str): scenario of MultiAgentEnv
          env_num (int): number of parallel envs to train
          seed (int): random seed
      """
      self.envs = [
          MPEEnv(scenario_name, seed + rank * 1000)
          for rank in range(env_num)
      ]
      self.raw_env = self.envs[0].env
      self.share_observation_space = (
          self.raw_env.share_observation_space
      )
      self.observation_space = self.raw_env.observation_space
      self.action_space = self.raw_env.action_space
      self.num_envs = env_num
  
file_path: PARL/benchmark/torch/mappo/env_wrappers.py
imports: import numpy
  from multiagent.environment import MultiAgentEnv
  from multiagent.scenarios import load
label_desc: Use image node_id 1 to create a new VectorEnv object from inherited base classes: object with example: obj = VectorEnv(scenario_name, env_num, seed)