add to_string for samplers (#4484)

Co-authored-by: Chris Elion <chris.elion@unity3d.com>

ml-agents/mlagents/trainers/environment_parameter_manager.py

             lesson_num = GlobalTrainingStatus.get_parameter_state(
                 param_name, StatusType.LESSON_NUM
             )
+            next_lesson_num = lesson_num + 1
-                and len(settings.curriculum) > lesson_num + 1
+                and len(settings.curriculum) > next_lesson_num
             ):
                 behavior_to_consider = lesson.completion_criteria.behavior
                 if behavior_to_consider in trainer_steps:
                     self._smoothed_values[param_name] = new_smoothing
                     if must_increment:
                         GlobalTrainingStatus.set_parameter_state(
-                            param_name, StatusType.LESSON_NUM, lesson_num + 1
+                            param_name, StatusType.LESSON_NUM, next_lesson_num
-                        new_lesson_name = settings.curriculum[lesson_num + 1].name
+                        new_lesson_name = settings.curriculum[next_lesson_num].name
+                        new_lesson_value = settings.curriculum[next_lesson_num].value
+
-                            f"Parameter '{param_name}' has changed. Now in lesson '{new_lesson_name}'"
+                            f"Parameter '{param_name}' has been updated to {new_lesson_value}."
+                            + f" Now in lesson '{new_lesson_name}'"
                         )
                         updated = True
                         if lesson.completion_criteria.require_reset:

ml-agents/mlagents/trainers/settings.py

 class ParameterRandomizationSettings(abc.ABC):
     seed: int = parser.get_default("seed")
 
+    def __str__(self) -> str:
+        """
+        Helper method to output sampler stats to console.
+        """
+        raise TrainerConfigError(f"__str__ not implemented for type {self.__class__}.")
+
     @staticmethod
     def structure(
         d: Union[Mapping, float], t: type
 class ConstantSettings(ParameterRandomizationSettings):
     value: float = 0.0
 
+    def __str__(self) -> str:
+        """
+        Helper method to output sampler stats to console.
+        """
+        return f"Float: value={self.value}"
+
     def apply(self, key: str, env_channel: EnvironmentParametersChannel) -> None:
         """
         Helper method to send sampler settings over EnvironmentParametersChannel
     min_value: float = attr.ib()
     max_value: float = 1.0
 
+    def __str__(self) -> str:
+        """
+        Helper method to output sampler stats to console.
+        """
+        return f"Uniform sampler: min={self.min_value}, max={self.max_value}"
+
     @min_value.default
     def _min_value_default(self):
         return 0.0
     mean: float = 1.0
     st_dev: float = 1.0
 
+    def __str__(self) -> str:
+        """
+        Helper method to output sampler stats to console.
+        """
+        return f"Gaussian sampler: mean={self.mean}, stddev={self.st_dev}"
+
     def apply(self, key: str, env_channel: EnvironmentParametersChannel) -> None:
         """
         Helper method to send sampler settings over EnvironmentParametersChannel
 @attr.s(auto_attribs=True)
 class MultiRangeUniformSettings(ParameterRandomizationSettings):
     intervals: List[Tuple[float, float]] = attr.ib()
+
+    def __str__(self) -> str:
+        """
+        Helper method to output sampler stats to console.
+        """
+        return f"MultiRangeUniform sampler: intervals={self.intervals}"
 
     @intervals.default
     def _intervals_default(self):

ml-agents/mlagents/trainers/tests/test_settings.py

     assert isinstance(
         env_param_settings["length"].curriculum[0].value, MultiRangeUniformSettings
     )
+
+    # Check __str__ is correct
+    assert (
+        str(env_param_settings["mass"].curriculum[0].value)
+        == "Uniform sampler: min=1.0, max=2.0"
+    )
+    assert (
+        str(env_param_settings["scale"].curriculum[0].value)
+        == "Gaussian sampler: mean=1.0, stddev=2.0"
+    )
+    assert (
+        str(env_param_settings["length"].curriculum[0].value)
+        == "MultiRangeUniform sampler: intervals=[(1.0, 2.0), (3.0, 4.0)]"
+    )
+    assert str(env_param_settings["gravity"].curriculum[0].value) == "Float: value=1"
+
     assert isinstance(
         env_param_settings["wall_height"].curriculum[0].value, ConstantSettings
     )