diff --git a/docs/guide/integrations.rst b/docs/guide/integrations.rst
index 3f7619d..49bbdb2 100644
--- a/docs/guide/integrations.rst
+++ b/docs/guide/integrations.rst
@@ -22,7 +22,7 @@ The full documentation is available here: https://docs.wandb.ai/guides/integrati
   config = {
       "policy_type": "MlpPolicy",
       "total_timesteps": 25000,
-      "env_name": "CartPole-v1",
+      "env_id": "CartPole-v1",
   }
   run = wandb.init(
       project="sb3",
@@ -32,7 +32,7 @@ The full documentation is available here: https://docs.wandb.ai/guides/integrati
       # save_code=True,  # optional
   )
 
-  model = PPO(config["policy_type"], config["env_name"], verbose=1, tensorboard_log=f"runs/{run.id}")
+  model = PPO(config["policy_type"], config["env_id"], verbose=1, tensorboard_log=f"runs/{run.id}")
   model.learn(
       total_timesteps=config["total_timesteps"],
       callback=WandbCallback(
diff --git a/tests/test_callbacks.py b/tests/test_callbacks.py
index a0e20b7..420a16a 100644
--- a/tests/test_callbacks.py
+++ b/tests/test_callbacks.py
@@ -20,19 +20,26 @@ from stable_baselines3.common.envs import BitFlippingEnv, IdentityEnv
 from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
 
 
+def select_env(model_class) -> str:
+    if model_class is DQN:
+        return "CartPole-v1"
+    else:
+        return "Pendulum-v1"
+
+
 @pytest.mark.parametrize("model_class", [A2C, PPO, SAC, TD3, DQN, DDPG])
 def test_callbacks(tmp_path, model_class):
     log_folder = tmp_path / "logs/callbacks/"
 
     # DQN only support discrete actions
-    env_name = select_env(model_class)
+    env_id = select_env(model_class)
     # Create RL model
     # Small network for fast test
-    model = model_class("MlpPolicy", env_name, policy_kwargs=dict(net_arch=[32]))
+    model = model_class("MlpPolicy", env_id, policy_kwargs=dict(net_arch=[32]))
 
     checkpoint_callback = CheckpointCallback(save_freq=1000, save_path=log_folder)
 
-    eval_env = gym.make(env_name)
+    eval_env = gym.make(env_id)
     # Stop training if the performance is good enough
     callback_on_best = StopTrainingOnRewardThreshold(reward_threshold=-1200, verbose=1)
 
@@ -82,7 +89,7 @@ def test_callbacks(tmp_path, model_class):
         n_envs = 2
         # Pendulum-v1 has a timelimit of 200 timesteps
         max_episode_length = 200
-        envs = make_vec_env(env_name, n_envs=n_envs, seed=0)
+        envs = make_vec_env(env_id, n_envs=n_envs, seed=0)
 
         model = model_class("MlpPolicy", envs, policy_kwargs=dict(net_arch=[32]))
 
@@ -100,13 +107,6 @@ def test_callbacks(tmp_path, model_class):
         shutil.rmtree(log_folder)
 
 
-def select_env(model_class) -> str:
-    if model_class is DQN:
-        return "CartPole-v1"
-    else:
-        return "Pendulum-v1"
-
-
 def test_eval_callback_vec_env():
     # tests that eval callback does not crash when given a vector
     n_eval_envs = 3
@@ -153,17 +153,17 @@ def test_eval_callback_logs_are_written_with_the_correct_timestep(tmp_path):
     pytest.importorskip("tensorboard")
     from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
 
-    env_name = select_env(DQN)
+    env_id = select_env(DQN)
     model = DQN(
         "MlpPolicy",
-        env_name,
+        env_id,
         policy_kwargs=dict(net_arch=[32]),
         tensorboard_log=tmp_path,
         verbose=1,
         seed=1,
     )
 
-    eval_env = gym.make(env_name)
+    eval_env = gym.make(env_id)
     eval_freq = 101
     eval_callback = EvalCallback(eval_env, eval_freq=eval_freq, warn=False)
     model.learn(500, callback=eval_callback)
diff --git a/tests/test_predict.py b/tests/test_predict.py
index 22ff4fd..6343e2f 100644
--- a/tests/test_predict.py
+++ b/tests/test_predict.py
@@ -40,10 +40,10 @@ def test_auto_wrap(model_class):
     """Test auto wrapping of env into a VecEnv."""
     # Use different environment for DQN
     if model_class is DQN:
-        env_name = "CartPole-v1"
+        env_id = "CartPole-v1"
     else:
-        env_name = "Pendulum-v1"
-    env = gym.make(env_name)
+        env_id = "Pendulum-v1"
+    env = gym.make(env_id)
     model = model_class("MlpPolicy", env)
     model.learn(100)