code: import gym
  import numpy as np
  import gym_reacher
  
  from stable_baselines.ddpg.policies import MlpPolicy
  from stable_baselines.common.noise import NormalActionNoise, OrnsteinUhlenbeckActionNoise, AdaptiveParamNoiseSpec
  from stable_baselines import DDPG
  
  # env = gym.make('MountainCarContinuous-v0')
  env = gym.make('Reacher1Dof-v0')
  
  # the noise objects for DDPG
  n_actions = env.action_space.shape[-1]
  param_noise = None
  action_noise = OrnsteinUhlenbeckActionNoise(mean=np.zeros(n_actions), sigma=float(0.5) * np.ones(n_actions))
  
  model = DDPG(MlpPolicy, env, verbose=1, param_noise=param_noise, action_noise=action_noise)
  model.learn(total_timesteps=10000)
  model.save("ddpg_mountain")
  
apis: numpy.ones numpy.zeros