Fix CI for macos-latest

.github/workflows/build.yml

@@ -1,71 +1,81 @@
 name: build
 
-on: [push, pull_request]
+on: [pull_request]
 
 jobs:
   test:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ubuntu-latest, macos-latest, windows-latest]
+        os: [ubuntu-latest, macos-latest, macos-13, windows-latest]
         python-version: ['3.7', '3.8', '3.9', '3.10']
+        exclude:
+          # Exclude the combination of macOS-latest and Python 3.7 as arm64 doesn't support Python 3.7
+          - os: macos-latest
+            python-version: '3.7'
     steps:
-    - uses: actions/checkout@v3
+    # Check out the repository code
+    - uses: actions/checkout@v4
+
+    # Set up the specified Python version
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
+
+    # Install dependencies
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
         pip install -e .
         pip install pytest-cov
+
+    # Run tests with pytest and generate coverage report
     - name: Test with pytest
       run: |
         pytest --cov=./ --cov-report=xml
+
+    # Additional steps only for ubuntu-latest and Python 3.10
+    # Upload the coverage report as an artifact
     - name: Save coverage report
-      uses: actions/upload-artifact@v2
+      if: matrix.os == 'ubuntu-latest' && matrix.python-version == '3.10'
+      uses: actions/upload-artifact@v4
       with:
         name: coverage-report
         path: ./coverage.xml
 
-  upload-coverage:
-    needs: test
-    runs-on: ubuntu-latest
-    steps:
-    - name: Checkout code
-      uses: actions/checkout@v2
-    - name: Download coverage reports
-      uses: actions/download-artifact@v2
-      with:
-        name: coverage-report
-    - name: Upload coverage to Codecov
-      uses: codecov/codecov-action@v3
-      with:
-        directory: ./coverage/reports/
-        fail_ci_if_error: true
-        files: ./coverage.xml
-        name: codecov-umbrella
-        verbose: true
-  codestyle_type_and_doc:
-    runs-on: ubuntu-latest
-    steps:
-    - uses: actions/checkout@v3
-    - name: Set up Python 3.8
-      uses: actions/setup-python@v4
-      with:
-        python-version: 3.8
-    - name: Install dependencies
+    # Install development dependencies
+    - name: Install dev dependencies
+      if: matrix.os == 'ubuntu-latest' && matrix.python-version == '3.10'
       run: |
-        python -m pip install --upgrade pip
         pip install -e .[develop]
+
+    # Run Pytype for type checking
     - name: Pytype
+      if: matrix.os == 'ubuntu-latest' && matrix.python-version == '3.10'
       run: |
         pytype panda_gym
+
+    # Check code style with black and isort
     - name: Check codestyle
+      if: matrix.os == 'ubuntu-latest' && matrix.python-version == '3.10'
       run: |
         black -l 127 --check panda_gym test
         isort -l 127 --profile black --check panda_gym test
+
+    # Build documentation
     - name: Make docs
+      if: matrix.os == 'ubuntu-latest' && matrix.python-version == '3.10'
       run: |
         make html
+
+    # Upload coverage to Codecov
+    - name: Upload coverage to Codecov
+      if: matrix.os == 'ubuntu-latest' && matrix.python-version == '3.10'
+      uses: codecov/codecov-action@v4
+      with:
+        files: ./coverage.xml
+        fail_ci_if_error: true
+        name: codecov-umbrella
+        verbose: true
+        token: ${{ secrets.CODECOV_TOKEN }} # required

panda_gym/envs/tasks/flip.py

@@ -78,11 +78,11 @@ def _sample_object(self) -> Tuple[np.ndarray, np.ndarray]:
         object_rotation = np.zeros(3)
         return object_position, object_rotation
 
-    def is_success(self, achieved_goal: np.ndarray, desired_goal: np.ndarray) -> np.ndarray:
+    def is_success(self, achieved_goal: np.ndarray, desired_goal: np.ndarray, info: Dict[str, Any] = {}) -> np.ndarray:
         d = angle_distance(achieved_goal, desired_goal)
         return np.array(d < self.distance_threshold, dtype=bool)
 
-    def compute_reward(self, achieved_goal, desired_goal, info: Dict[str, Any]) -> np.ndarray:
+    def compute_reward(self, achieved_goal: np.ndarray, desired_goal: np.ndarray, info: Dict[str, Any] = {}) -> np.ndarray:
         d = angle_distance(achieved_goal, desired_goal)
         if self.reward_type == "sparse":
             return -np.array(d > self.distance_threshold, dtype=np.float32)

panda_gym/envs/tasks/pick_and_place.py

@@ -83,11 +83,11 @@ def _sample_object(self) -> np.ndarray:
         object_position += noise
         return object_position
 
-    def is_success(self, achieved_goal: np.ndarray, desired_goal: np.ndarray) -> np.ndarray:
+    def is_success(self, achieved_goal: np.ndarray, desired_goal: np.ndarray, info: Dict[str, Any] = {}) -> np.ndarray:
         d = distance(achieved_goal, desired_goal)
         return np.array(d < self.distance_threshold, dtype=bool)
 
-    def compute_reward(self, achieved_goal, desired_goal, info: Dict[str, Any]) -> np.ndarray:
+    def compute_reward(self, achieved_goal: np.ndarray, desired_goal: np.ndarray, info: Dict[str, Any] = {}) -> np.ndarray:
         d = distance(achieved_goal, desired_goal)
         if self.reward_type == "sparse":
             return -np.array(d > self.distance_threshold, dtype=np.float32)

panda_gym/envs/tasks/push.py

@@ -85,11 +85,11 @@ def _sample_object(self) -> np.ndarray:
         object_position += noise
         return object_position
 
-    def is_success(self, achieved_goal: np.ndarray, desired_goal: np.ndarray) -> np.ndarray:
+    def is_success(self, achieved_goal: np.ndarray, desired_goal: np.ndarray, info: Dict[str, Any] = {}) -> np.ndarray:
         d = distance(achieved_goal, desired_goal)
         return np.array(d < self.distance_threshold, dtype=bool)
 
-    def compute_reward(self, achieved_goal, desired_goal, info: Dict[str, Any]) -> np.ndarray:
+    def compute_reward(self, achieved_goal: np.ndarray, desired_goal: np.ndarray, info: Dict[str, Any] = {}) -> np.ndarray:
         d = distance(achieved_goal, desired_goal)
         if self.reward_type == "sparse":
             return -np.array(d > self.distance_threshold, dtype=np.float32)

panda_gym/envs/tasks/reach.py

@@ -52,11 +52,11 @@ def _sample_goal(self) -> np.ndarray:
         goal = self.np_random.uniform(self.goal_range_low, self.goal_range_high)
         return goal
 
-    def is_success(self, achieved_goal: np.ndarray, desired_goal: np.ndarray) -> np.ndarray:
+    def is_success(self, achieved_goal: np.ndarray, desired_goal: np.ndarray, info: Dict[str, Any] = {}) -> np.ndarray:
         d = distance(achieved_goal, desired_goal)
         return np.array(d < self.distance_threshold, dtype=bool)
 
-    def compute_reward(self, achieved_goal, desired_goal, info: Dict[str, Any]) -> np.ndarray:
+    def compute_reward(self, achieved_goal: np.ndarray, desired_goal: np.ndarray, info: Dict[str, Any] = {}) -> np.ndarray:
         d = distance(achieved_goal, desired_goal)
         if self.reward_type == "sparse":
             return -np.array(d > self.distance_threshold, dtype=np.float32)

panda_gym/envs/tasks/slide.py

@@ -89,11 +89,11 @@ def _sample_object(self) -> np.ndarray:
         object_position += noise
         return object_position
 
-    def is_success(self, achieved_goal: np.ndarray, desired_goal: np.ndarray) -> np.ndarray:
+    def is_success(self, achieved_goal: np.ndarray, desired_goal: np.ndarray, info: Dict[str, Any] = {}) -> np.ndarray:
         d = distance(achieved_goal, desired_goal)
         return np.array(d < self.distance_threshold, dtype=bool)
 
-    def compute_reward(self, achieved_goal, desired_goal, info: Dict[str, Any]) -> np.ndarray:
+    def compute_reward(self, achieved_goal: np.ndarray, desired_goal: np.ndarray, info: Dict[str, Any] = {}) -> np.ndarray:
         d = distance(achieved_goal, desired_goal)
         if self.reward_type == "sparse":
             return -np.array(d > self.distance_threshold, dtype=np.float32)

panda_gym/envs/tasks/stack.py

@@ -117,12 +117,12 @@ def _sample_objects(self) -> Tuple[np.ndarray, np.ndarray]:
         # if distance(object1_position, object2_position) > 0.1:
         return object1_position, object2_position
 
-    def is_success(self, achieved_goal: np.ndarray, desired_goal: np.ndarray) -> np.ndarray:
+    def is_success(self, achieved_goal: np.ndarray, desired_goal: np.ndarray, info: Dict[str, Any] = {}) -> np.ndarray:
         # must be vectorized !!
         d = distance(achieved_goal, desired_goal)
         return np.array((d < self.distance_threshold), dtype=bool)
 
-    def compute_reward(self, achieved_goal, desired_goal, info: Dict[str, Any]) -> np.ndarray:
+    def compute_reward(self, achieved_goal: np.ndarray, desired_goal: np.ndarray, info: Dict[str, Any] = {}) -> np.ndarray:
         d = distance(achieved_goal, desired_goal)
         if self.reward_type == "sparse":
             return -np.array((d > self.distance_threshold), dtype=np.float32)