diff --git a/docs/misc/changelog.rst b/docs/misc/changelog.rst
index b4355c7..26b147b 100644
--- a/docs/misc/changelog.rst
+++ b/docs/misc/changelog.rst
@@ -39,6 +39,7 @@ Others:
 - Fixed flake8 config to be compatible with flake8 6+
 - Goal-conditioned environments are now characterized by the availability of the ``compute_reward`` method, rather than by their inheritance to ``gym.GoalEnv``
 - Replaced ``CartPole-v0`` by ``CartPole-v1`` is tests
+- Fixed ``tests/test_distributions.py`` type hint
 
 Documentation:
 ^^^^^^^^^^^^^^
diff --git a/setup.cfg b/setup.cfg
index 5e30726..5e41364 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -72,7 +72,6 @@ exclude = (?x)(
     | stable_baselines3/sac/sac.py$
     | stable_baselines3/td3/policies.py$
     | stable_baselines3/td3/td3.py$
-    | tests/test_distributions.py$
     | tests/test_logger.py$
     | tests/test_tensorboard.py$
     | tests/test_train_eval_mode.py$
diff --git a/stable_baselines3/common/distributions.py b/stable_baselines3/common/distributions.py
index 63eb475..b78ef82 100644
--- a/stable_baselines3/common/distributions.py
+++ b/stable_baselines3/common/distributions.py
@@ -679,7 +679,7 @@ def make_proba_distribution(
     elif isinstance(action_space, spaces.Discrete):
         return CategoricalDistribution(action_space.n, **dist_kwargs)
     elif isinstance(action_space, spaces.MultiDiscrete):
-        return MultiCategoricalDistribution(action_space.nvec, **dist_kwargs)
+        return MultiCategoricalDistribution(list(action_space.nvec), **dist_kwargs)
     elif isinstance(action_space, spaces.MultiBinary):
         return BernoulliDistribution(action_space.n, **dist_kwargs)
     else:
diff --git a/tests/test_distributions.py b/tests/test_distributions.py
index 513429b..e782182 100644
--- a/tests/test_distributions.py
+++ b/tests/test_distributions.py
@@ -55,7 +55,7 @@ def test_squashed_gaussian(model_class):
 
 
 @pytest.fixture()
-def dummy_model_distribution_obs_and_actions() -> Tuple[A2C, np.array, np.array]:
+def dummy_model_distribution_obs_and_actions() -> Tuple[A2C, np.ndarray, np.ndarray]:
     """
     Fixture creating a Pendulum-v1 gym env, an A2C model and sampling 10 random observations and actions from the env
     :return: A2C model, random observations, random actions
@@ -165,9 +165,7 @@ def test_categorical(dist, CAT_ACTIONS):
         BernoulliDistribution(N_ACTIONS).proba_distribution(th.rand(N_ACTIONS)),
         CategoricalDistribution(N_ACTIONS).proba_distribution(th.rand(N_ACTIONS)),
         DiagGaussianDistribution(N_ACTIONS).proba_distribution(th.rand(N_ACTIONS), th.rand(N_ACTIONS)),
-        MultiCategoricalDistribution(np.array([N_ACTIONS, N_ACTIONS])).proba_distribution(
-            th.rand(1, sum([N_ACTIONS, N_ACTIONS]))
-        ),
+        MultiCategoricalDistribution([N_ACTIONS, N_ACTIONS]).proba_distribution(th.rand(1, sum([N_ACTIONS, N_ACTIONS]))),
         SquashedDiagGaussianDistribution(N_ACTIONS).proba_distribution(th.rand(N_ACTIONS), th.rand(N_ACTIONS)),
         StateDependentNoiseDistribution(N_ACTIONS).proba_distribution(
             th.rand(N_ACTIONS), th.rand([N_ACTIONS, N_ACTIONS]), th.rand([N_ACTIONS, N_ACTIONS])