title: gym car racing v0 using DQN
body: <p>I am currently learning reinforcement learning and wanted to use it on the car racing-v0 environment. I have successfully made it using PPO algorithm and now I want to use a DQN algorithm but when I want to train the model it gives me this error:</p>
  <blockquote>
  <p>AssertionError: The algorithm only supports (&lt;class 'gym.spaces.discrete.Discrete'&gt;,) as action spaces but Box([-1.  0.  0.], [1. 1. 1.], (3,), float32) was provided</p>
  </blockquote>
  <p>Here is my code:</p>
  <pre><code>import os
  import gym
  from stable_baselines3 import DQN
  from stable_baselines3.common.vec_env import DummyVecEnv
  from stable_baselines3.common.evaluation import evaluate_policy
  
  environment_name = 'CarRacing-v0'
  env = gym.make(environment_name)
  
  #Test Environment
  episodes = 5
  for episode in range(1, episodes+1):
      obs = env.reset()
      done = False
      score = 0
      
      while not done:
          env.render()
          action = env.action_space.sample()
          obs, reward, done, info = env.step(action)
          score += reward
      print('Episode:{} Score:{}'.format(episode, score))
  env.close()
  
  env = gym.make(environment_name)
  env = DummyVecEnv([lambda: env])
  
  log_path = os.path.join('Training', 'Logs')
  model = DQN('CnnPolicy', env, verbose=1, tensorboard_log = log_path)
  
  </code></pre>
  <p>I am using jupyter notebook for this project</p>