DQN Stable baseline 3 doesn't start training

Question

I created a custom environment (I checked it through the check_env() function provided by Stable baseline and it's fine). This is the code I use to start the training with DQN

from stable_baselines3 import DQN
from stable_baselines3.common.env_checker import check_env as sb3_check_env
import torch

import wandb
from wandb.integration.sb3 import WandbCallback
from gym.version import VERSION
from utils.clustering_phase import load_merged_dataframe
from utils.constants import DATASET_PREDICTION_PATH

from environments.ns_env import NS3OfflineEnv
from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3.common.evaluation import evaluate_policy

if __name__ == "__main__":
    # Set pytorch num threads to 1 for faster training.
    torch.set_num_threads(1)
    WANDB_NOTEBOOK_NAME = 'ES' #my notebook
    WANDB_API_KEY= '1234567890'  #my KEY (not showing the real one)

    vec_env = OfflineEnv(data="observation_df.csv")

    verbosity=2
    
    config = {
        "policy_type": "MlpPolicy",
        "total_timesteps": 100
    }

    name='100-dqn'
    run = wandb.init(
        project="ProjectName",
        config=config,
        sync_tensorboard=True,
        monitor_gym=True,
        name=name
    )

    model = DQN(policy=config["policy_type"], env=vec_env,
                batch_size=256,
                verbose=verbosity, 
                tensorboard_log=f"wandb/runs/{name}{run.id}",
                )

    model.learn(
        total_timesteps=config["total_timesteps"],
        callback=[WandbCallback(
            gradient_save_freq=100,
            model_save_freq=100,
            model_save_path=f"./models/ppo/{name}{run.id}",
            verbose=verbosity)]
    )

    run.finish()

And from the moment I execute the Python file I get the following

Moreover, on WanDB I'm only getting the "System plots" and nothing about training, so I suppose the training doesn't start at all

What is wrong in my training code?