Fix lesson incrementing (#4279)

ml-agents/mlagents/trainers/environment_parameter_manager.py

             lesson = settings.curriculum[lesson_num]
             if (
                 lesson.completion_criteria is not None
-                and len(settings.curriculum) > lesson_num
+                and len(settings.curriculum) > lesson_num + 1
             ):
                 behavior_to_consider = lesson.completion_criteria.behavior
                 if behavior_to_consider in trainer_steps:

ml-agents/mlagents/trainers/exception.py

     pass
 
 
+class TrainerConfigWarning(Warning):
+    """
+    Any warning related to the configuration of trainers in the ML-Agents Toolkit.
+    """
+
+    pass
+
+
 class CurriculumError(TrainerError):
     """
     Any error related to training with a curriculum.

ml-agents/mlagents/trainers/settings.py

+import warnings
+
 import attr
 import cattr
 from typing import Dict, Optional, List, Any, DefaultDict, Mapping, Tuple, Union
 
 from mlagents.trainers.cli_utils import StoreConfigFile, DetectDefault, parser
 from mlagents.trainers.cli_utils import load_config
-from mlagents.trainers.exception import TrainerConfigError
+from mlagents.trainers.exception import TrainerConfigError, TrainerConfigWarning
 from mlagents.trainers.models import ScheduleType, EncoderType
 
 from mlagents_envs import logging_util
     def _check_lesson_chain(lessons, parameter_name):
         """
         Ensures that when using curriculum, all non-terminal lessons have a valid
-        CompletionCriteria
+        CompletionCriteria, and that the terminal lesson does not contain a CompletionCriteria.
         """
         num_lessons = len(lessons)
         for index, lesson in enumerate(lessons):
+                )
+            if index == num_lessons - 1 and lesson.completion_criteria is not None:
+                warnings.warn(
+                    f"Your final lesson definition contains completion_criteria for {parameter_name}."
+                    f"It will be ignored.",
+                    TrainerConfigWarning,
                 )
 
     @staticmethod

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

 import yaml
 
 
-from mlagents.trainers.exception import TrainerConfigError
+from mlagents.trainers.exception import TrainerConfigError, TrainerConfigWarning
 from mlagents.trainers.environment_parameter_manager import EnvironmentParameterManager
 from mlagents.trainers.settings import (
     RunOptions,
 """
 
 
+test_bad_curriculum_all_competion_criteria_config_yaml = """
+environment_parameters:
+    param_1:
+      curriculum:
+          - name: Lesson1
+            completion_criteria:
+                measure: reward
+                behavior: fake_behavior
+                threshold: 30
+                min_lesson_length: 100
+                require_reset: true
+            value: 1
+          - name: Lesson2
+            completion_criteria:
+                measure: reward
+                behavior: fake_behavior
+                threshold: 30
+                min_lesson_length: 100
+                require_reset: true
+            value: 2
+          - name: Lesson3
+            completion_criteria:
+                measure: reward
+                behavior: fake_behavior
+                threshold: 30
+                min_lesson_length: 100
+                require_reset: true
+            value:
+                sampler_type: uniform
+                sampler_parameters:
+                    min_value: 1
+                    max_value: 3
+"""
+
+
+
+
+def test_curriculum_raises_all_completion_criteria_conversion():
+    with pytest.warns(TrainerConfigWarning):
+        run_options = RunOptions.from_dict(
+            yaml.safe_load(test_bad_curriculum_all_competion_criteria_config_yaml)
+        )
+
+        param_manager = EnvironmentParameterManager(
+            run_options.environment_parameters, 1337, False
+        )
+        assert param_manager.update_lessons(
+            trainer_steps={"fake_behavior": 500},
+            trainer_max_steps={"fake_behavior": 1000},
+            trainer_reward_buffer={"fake_behavior": [1000] * 101},
+        ) == (True, True)
+        assert param_manager.update_lessons(
+            trainer_steps={"fake_behavior": 500},
+            trainer_max_steps={"fake_behavior": 1000},
+            trainer_reward_buffer={"fake_behavior": [1000] * 101},
+        ) == (True, True)
+        assert param_manager.update_lessons(
+            trainer_steps={"fake_behavior": 500},
+            trainer_max_steps={"fake_behavior": 1000},
+            trainer_reward_buffer={"fake_behavior": [1000] * 101},
+        ) == (False, False)
+        assert param_manager.get_current_lesson_number() == {"param_1": 2}
 
 
 test_everything_config_yaml = """