diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index b1078cd285..113b9d7bdb 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -5,10 +5,10 @@ name: CI
 
 on:
   push:
-    branches: [ master ]
+    branches: [master]
   pull_request:
-    branches: [ master ]
-
+    branches: [master]
+  workflow_dispatch:
 jobs:
   build:
     env:
@@ -23,38 +23,38 @@ jobs:
         python-version: ["3.8", "3.9", "3.10", "3.11"]
 
     steps:
-    - uses: actions/checkout@v3
-    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v4
-      with:
-        python-version: ${{ matrix.python-version }}
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        # cpu version of pytorch
-        pip install torch==2.1.0 --index-url https://download.pytorch.org/whl/cpu
+      - uses: actions/checkout@v3
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          # cpu version of pytorch
+          pip install torch==2.1.0 --index-url https://download.pytorch.org/whl/cpu
 
-        # Install Atari Roms
-        pip install autorom
-        wget https://gist.githubusercontent.com/jjshoots/61b22aefce4456920ba99f2c36906eda/raw/00046ac3403768bfe45857610a3d333b8e35e026/Roms.tar.gz.b64
-        base64 Roms.tar.gz.b64 --decode &> Roms.tar.gz
-        AutoROM --accept-license --source-file Roms.tar.gz
+          # Install Atari Roms
+          pip install autorom
+          wget https://gist.githubusercontent.com/jjshoots/61b22aefce4456920ba99f2c36906eda/raw/00046ac3403768bfe45857610a3d333b8e35e026/Roms.tar.gz.b64
+          base64 Roms.tar.gz.b64 --decode &> Roms.tar.gz
+          AutoROM --accept-license --source-file Roms.tar.gz
 
-        pip install .[extra_no_roms,tests,docs]
-        # Use headless version
-        pip install opencv-python-headless
-    - name: Lint with ruff
-      run: |
-        make lint
-    - name: Build the doc
-      run: |
-        make doc
-    - name: Check codestyle
-      run: |
-        make check-codestyle
-    - name: Type check
-      run: |
-        make type
-    - name: Test with pytest
-      run: |
-        make pytest
+          pip install .[extra_no_roms,tests,docs]
+          # Use headless version
+          pip install opencv-python-headless
+      - name: Lint with ruff
+        run: |
+          make lint
+      - name: Build the doc
+        run: |
+          make doc
+      - name: Check codestyle
+        run: |
+          make check-codestyle
+      - name: Type check
+        run: |
+          make type
+      - name: Test with pytest
+        run: |
+          make pytest
diff --git a/stable_baselines3/common/vec_env/vec_normalize.py b/stable_baselines3/common/vec_env/vec_normalize.py
index cda2370aa0..ca0a980127 100644
--- a/stable_baselines3/common/vec_env/vec_normalize.py
+++ b/stable_baselines3/common/vec_env/vec_normalize.py
@@ -125,6 +125,20 @@ def _sanity_checks(self) -> None:
                 f"not {self.observation_space}"
             )
 
+    @staticmethod
+    def _maybe_cast_reward(reward: np.ndarray) -> np.ndarray:
+        """
+        Cast `np.float64` reward datatype to `np.float32`,
+        keep the others dtype unchanged.
+
+        :param dtype: The original action space dtype
+        :return: ``np.float32`` if the dtype was float64,
+            the original dtype otherwise.
+        """
+        if reward.dtype == np.float64:
+            return reward.astype(np.float32)
+        return reward
+
     def __getstate__(self) -> Dict[str, Any]:
         """
         Gets state for pickling.
@@ -254,7 +268,8 @@ def normalize_reward(self, reward: np.ndarray) -> np.ndarray:
         """
         if self.norm_reward:
             reward = np.clip(reward / np.sqrt(self.ret_rms.var + self.epsilon), -self.clip_reward, self.clip_reward)
-        return reward.astype(np.float32)
+
+        return self._maybe_cast_reward(reward)
 
     def unnormalize_obs(self, obs: Union[np.ndarray, Dict[str, np.ndarray]]) -> Union[np.ndarray, Dict[str, np.ndarray]]:
         # Avoid modifying by reference the original object