[gym_jiminy/common] Remove 'post_fn' argument from drift term cond.

.github/workflows/linux.yml

@@ -140,15 +140,13 @@ jobs:
       if: matrix.BUILD_TYPE != 'Debug' && matrix.PYTHON_VERSION == '3.10'
       run: |
         # Generate stubs
-        # FIXME: stubgen does not work with Numpy 2.X
+        # FIXME: stubgen does not work with Numpy 2.X without option `--no-analysis`
         # (see https://github.com/python/mypy/issues/17396)
-        "${PYTHON_EXECUTABLE}" -m pip install "numpy<2.0"
-        stubgen -p jiminy_py -o ${RootDir}/build/pypi/jiminy_py/src
+        stubgen -p jiminy_py -o ${RootDir}/build/pypi/jiminy_py/src --no-analysis
         "${PYTHON_EXECUTABLE}" "${RootDir}/build_tools/stubgen.py" \
           -o ${RootDir}/build/stubs --ignore-invalid=all jiminy_py
         cp ${RootDir}/build/stubs/jiminy_py-stubs/core/__init__.pyi \
            ${RootDir}/build/pypi/jiminy_py/src/jiminy_py/core/core.pyi
-        "${PYTHON_EXECUTABLE}" -m pip install --upgrade "numpy>=1.24" numba torch
 
         # Re-install jiminy with stubs
         cd "${RootDir}/build"

.github/workflows/macos.yml

@@ -142,10 +142,9 @@ jobs:
 
         # Generate stubs
         if [[ "${{ matrix.BUILD_TYPE }}" != 'Debug' && "${{ matrix.OS }}" != 'macos-13' ]] ; then
-          # FIXME: stubgen does not work with Numpy 2.X
+          # FIXME: stubgen does not work with Numpy 2.X without option `--no-analysis`
           # (see https://github.com/python/mypy/issues/17396)
-          "${PYTHON_EXECUTABLE}" -m pip install "numpy<2.0"
-          stubgen -p jiminy_py -o ${RootDir}/build/pypi/jiminy_py/src
+          stubgen -p jiminy_py -o ${RootDir}/build/pypi/jiminy_py/src --no-analysis
           # FIXME: Python 3.10 and Python 3.11 crashes when generating stubs without any backtrace...
           if [[ "${{ matrix.PYTHON_VERSION }}" != '3.10' && "${{ matrix.PYTHON_VERSION }}" != '3.11' ]] ; then
             # lldb --batch -o "settings set target.process.stop-on-exec false" \
@@ -155,7 +154,6 @@ jobs:
             cp ${RootDir}/build/stubs/jiminy_py-stubs/core/__init__.pyi \
               ${RootDir}/build/pypi/jiminy_py/src/jiminy_py/core/core.pyi
           fi
-          "${PYTHON_EXECUTABLE}" -m pip install --upgrade "numpy>=1.24" numba torch
         fi
 
         # Generate wheels

.github/workflows/manylinux.yml

@@ -112,15 +112,13 @@ jobs:
         export LD_LIBRARY_PATH="$InstallDir/lib:$InstallDir/lib64:/usr/local/lib"
 
         # Generate stubs
-        # FIXME: stubgen does not work with Numpy 2.X
+        # FIXME: stubgen does not work with Numpy 2.X without option `--no-analysis`
         # (see https://github.com/python/mypy/issues/17396)
-        "${PYTHON_EXECUTABLE}" -m pip install "numpy<2.0"
-        stubgen -p jiminy_py -o $RootDir/build/pypi/jiminy_py/src
+        stubgen -p jiminy_py -o $RootDir/build/pypi/jiminy_py/src --no-analysis
         "${PYTHON_EXECUTABLE}" "$RootDir/build_tools/stubgen.py" \
           -o $RootDir/build/stubs --ignore-invalid=all jiminy_py
         \cp $RootDir/build/stubs/jiminy_py-stubs/core/__init__.pyi \
             $RootDir/build/pypi/jiminy_py/src/jiminy_py/core/core.pyi
-        "${PYTHON_EXECUTABLE}" -m pip install --upgrade "numpy>=1.24" numba
 
         # Generate wheels
         cd "$RootDir/build"

.github/workflows/win.yml

@@ -123,17 +123,13 @@ jobs:
         ${env:Path} += ";$InstallDir/lib"
 
         # Generate stubs
-        if ("${{ matrix.PYTHON_VERSION }}" -ne "3.13") {
-          # FIXME: stubgen does not work with Numpy 2.X
-          # (see https://github.com/python/mypy/issues/17396)
-          python -m pip install "numpy<2.0"
-          stubgen -p jiminy_py -o $RootDir/build/pypi/jiminy_py/src
-          python "$RootDir/build_tools/stubgen.py" `
-            -o $RootDir/build/stubs --ignore-invalid=all jiminy_py
-          Copy-Item -Force -Path "$RootDir/build/stubs/jiminy_py-stubs/core/__init__.pyi" `
-                    -Destination "$RootDir/build/pypi/jiminy_py/src/jiminy_py/core/core.pyi"
-          python -m pip install --upgrade "numpy>=1.24" numba torch
-        }
+        # FIXME: stubgen does not work with Numpy 2.X without option `--no-analysis`
+        # (see https://github.com/python/mypy/issues/17396)
+        stubgen -p jiminy_py -o $RootDir/build/pypi/jiminy_py/src --no-analysis
+        python "$RootDir/build_tools/stubgen.py" `
+          -o $RootDir/build/stubs --ignore-invalid=all jiminy_py
+        Copy-Item -Force -Path "$RootDir/build/stubs/jiminy_py-stubs/core/__init__.pyi" `
+                  -Destination "$RootDir/build/pypi/jiminy_py/src/jiminy_py/core/core.pyi"
 
         # Generate wheels
         Set-Location -Path "$RootDir/build"

python/gym_jiminy/common/gym_jiminy/common/bases/__init__.py

@@ -24,7 +24,8 @@
                            MixtureReward,
                            AbstractTerminationCondition,
                            QuantityTermination,
-                           EpisodeState)
+                           EpisodeState,
+                           partial_hashable)
 from .blocks import (BlockState,
                      InterfaceBlock,
                      BaseObserverBlock,
@@ -74,5 +75,6 @@
     'QuantityCreator',
     'EpisodeState',
     'StateQuantity',
-    'DatasetTrajectoryQuantity'
+    'DatasetTrajectoryQuantity',
+    'partial_hashable'
 ]

python/gym_jiminy/common/gym_jiminy/common/bases/compositions.py

@@ -6,9 +6,13 @@
 This modular approach allows for standardization of usual metrics. Overall, it
 greatly reduces code duplication and bugs.
 """
+import inspect
+from functools import partial
 from abc import abstractmethod, ABCMeta
 from enum import IntEnum
-from typing import Tuple, Sequence, Callable, Union, Optional, Generic, TypeVar
+from typing import (
+    Tuple, Sequence, Callable, Union, Optional, Generic, Any, TypeVar,
+    TYPE_CHECKING)
 
 import numpy as np
 
@@ -25,6 +29,53 @@
 ArrayLikeOrScalar = Union[ArrayOrScalar, Sequence[Union[Number, np.number]]]
 
 
+class partial_hashable(partial):  # pylint: disable=invalid-name
+    """Extends standard `functools.Partial` class with hash and equality
+    operator.
+
+    Two partial instances are equal if they are wrapping the exact same
+    function (i.e. pointing to the same memory address as per `id` build-in
+    function), and bindings the same arguments (i.e. all arguments are equal
+    as per `==` operator). Note that it does not matter if the constructor
+    arguments of `Partial` itself are positional or keyword-based. Internally,
+    they will be stored in an ordered list of keyword-only arguments for
+    equality check.
+
+    .. warning::
+        Try to instantiate this class with invalid arguments for the method
+        being wrapped (e.g. specifying multiple values for the same argument)
+        would raise a `TypeError` exception, unlike `functools.partial` that
+        would only fail when calling the resulting callable object.
+    """
+
+    if TYPE_CHECKING:
+        _normalized_args: Tuple[Any, ...]
+
+    def __new__(cls,
+                func: Callable, /,
+                *args: Any,
+                **kwargs: Any) -> "partial_hashable":
+        # Call base implementation
+        self = super(partial_hashable, cls).__new__(cls, func, *args, **kwargs)
+
+        # Pre-compute normalized arguments once and for all
+        sig = inspect.signature(self.func)
+        bound = sig.bind_partial(*self.args, **(self.keywords or {}))
+        bound.apply_defaults()
+        self._normalized_args = tuple(bound.arguments.values())
+
+        return self
+
+    def __eq__(self, other: Any) -> bool:
+        if not isinstance(other, partial_hashable):
+            return False
+        return self.func == other.func and (
+            self._normalized_args == other._normalized_args)
+
+    def __hash__(self) -> int:
+        return hash((self.func, self._normalized_args))
+
+
 class AbstractReward(metaclass=ABCMeta):
     """Abstract class from which all reward component must derived.
 
@@ -514,7 +565,7 @@ def __call__(self, info: InfoType) -> Tuple[bool, bool]:
         return is_terminated, is_truncated
 
 
-class QuantityTermination(AbstractTerminationCondition, Generic[ValueT]):
+class QuantityTermination(AbstractTerminationCondition):
     """Convenience class making it easy to derive termination conditions from
     generic quantities.
 

python/gym_jiminy/common/gym_jiminy/common/compositions/__init__.py

@@ -7,6 +7,7 @@
 from .generic import (SurviveReward,
                       TrackingQuantityReward,
                       TrackingActuatedJointPositionsReward,
+                      MinimizeMechanicalPowerConsumption,
                       DriftTrackingQuantityTermination,
                       ShiftTrackingQuantityTermination,
                       MechanicalSafetyTermination,
@@ -38,6 +39,7 @@
     "SurviveReward",
     "MinimizeFrictionReward",
     "MinimizeAngularMomentumReward",
+    "MinimizeMechanicalPowerConsumption",
     "TrackingQuantityReward",
     "TrackingActuatedJointPositionsReward",
     "TrackingBaseHeightReward",