text: import numpy as np
  from gym.envs.mujoco import HalfCheetahEnv, InvertedPendulumEnv, ReacherEnv
  from gym.spaces import Discrete
  
  from rllab.misc.instrument import VariantGenerator
  import rlkit.torch.pytorch_util as ptu
  from rlkit.envs.wrappers import NormalizedBoxEnv
  from rlkit.envs.meta_maze import Maze, MazeSampler
  from rlkit.launchers.launcher_util import setup_logger, set_seed
  from rlkit.torch.sac.policies import TanhGaussianPolicy, DiscretePolicy
  from rlkit.torch.sac.sac import MetaSoftActorCritic, NewMetaSoftActorCritic
  from rlkit.torch.networks import FlattenMlp
  
  from rlkit.envs import OnTheFlyEnvSampler
  
  import yaml
  import argparse
  import importlib
  import psutil
  import os
  import argparse
  
  
  def experiment(variant):
      # we have to generate the combinations for the env_specs
      env_specs = variant['env_specs']
      env_sampler = MazeSampler(env_specs)
      sample_env, _ = env_sampler()
      meta_params_dim = 0
      
      obs_dim = int(np.prod(sample_env.observation_space.shape))
      if isinstance(sample_env.action_space, Discrete):
          action_dim = int(sample_env.action_space.n)
      else:
          action_dim = int(np.prod(sample_env.action_space.shape))
  
      net_size = variant['net_size']
      qf = FlattenMlp(
          hidden_sizes=[net_size, net_size],
          input_size=obs_dim + meta_params_dim,
          output_size=action_dim,
      )
      vf = FlattenMlp(
          hidden_sizes=[net_size, net_size],
          input_size=obs_dim + meta_params_dim,
          output_size=1,
      )
      policy = DiscretePolicy(
          hidden_sizes=[net_size, net_size],
          obs_dim=obs_dim + meta_params_dim,
          action_dim=action_dim,
      )
      algorithm = MetaSoftActorCritic(
          env_sampler=env_sampler,
          policy=policy,
          qf=qf,
          vf=vf,
          **variant['algo_params']
      )
      # assert False, "Have not added new sac yet!"
      if ptu.gpu_enabled():
          algorithm.cuda()
      algorithm.train()
  
      return 1
  
  
  if __name__ == '__main__':
      # Arguments
      parser = argparse.ArgumentParser()
      parser.add_argument('-e', '--experiment', help='experiment specification file')
      args = parser.parse_args()
      with open(args.experiment, 'r') as spec_file:
          spec_string = spec_file.read()
          exp_specs = yaml.load(spec_string)
      
      exp_id = exp_specs['exp_id']
      exp_prefix = exp_specs['exp_name']
      seed = exp_specs['seed']
      set_seed(seed)
      setup_logger(exp_prefix=exp_prefix, exp_id=exp_id, variant=exp_specs)
  
      experiment(exp_specs)
  