title: Stable_Baselines: How to transfer trained reinforcement learning models between devices?
body: <p>I've been training a reinforcement learning model on a kaggle notebook, using the gpu accelerator, since I only have a cpu laptop to work with. However when I try to import the model onto a local jupyter notebook, in order to render it, I get the following error.</p>
  <pre><code>AssertionError                            Traceback (most recent call last)
  ~\AppData\Local\Temp/ipykernel_13752/2941311152.py in &lt;module&gt;
        2 # device = torch.device('cpu')
        3 # model = PPO('CnnPolicy', env, verbose = 1, tensorboard_log = Log_Dir, learning_rate = 0.000001, n_steps = 512)
  ----&gt; 4 PPO.load('./Training/Saved Models/Mario Models/best_model_100000', env)
  
  c:\users\test\appdata\local\programs\python\python39\lib\site-packages\stable_baselines3\common\base_class.py in load(cls, path, env, device, custom_objects, print_system_info, force_reset, **kwargs)
      728         model.__dict__.update(data)
      729         model.__dict__.update(kwargs)
  --&gt; 730         model._setup_model()
      731 
      732         # put state_dicts back in place
  
  c:\users\test\appdata\local\programs\python\python39\lib\site-packages\stable_baselines3\ppo\ppo.py in _setup_model(self)
      156 
      157         # Initialize schedules for policy/value clipping
  --&gt; 158         self.clip_range = get_schedule_fn(self.clip_range)
      159         if self.clip_range_vf is not None:
      160             if isinstance(self.clip_range_vf, (float, int)):
  
  c:\users\test\appdata\local\programs\python\python39\lib\site-packages\stable_baselines3\common\utils.py in get_schedule_fn(value_schedule)
       89         value_schedule = constant_fn(float(value_schedule))
       90     else:
  ---&gt; 91         assert callable(value_schedule)
       92     return value_schedule
       93 
  
  AssertionError: 
  </code></pre>
  <p>I tried looking through documentation to correct the error, but the guidelines for transferring a standard pytorch model do not work on a stable baselines model.</p>