tests for settings

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

     RewardSignalType,
     RewardSignalSettings,
     CuriositySettings,
+    ParameterRandomizationSettings,
+    UniformSettings,
+    GaussianSettings,
+    MultiRangeUniformSettings,
     TrainerType,
     strict_to_cls,
 )
         RewardSignalSettings.structure(
             "notadict", Dict[RewardSignalType, RewardSignalSettings]
         )
+
+
+def test_parameter_randomization_structure():
+    """
+    Tests the ParameterRandomizationSettings structure method and all validators.
+    """
+    parameter_randomization_dict = {
+        "mass": {"uniform": {"min_value": 1.0, "max_value": 2.0}},
+        "scale": {"gaussian": {"mean": 1.0, "st_dev": 2.0}},
+        "length": {"multirangeuniform": {"intervals": [[1.0, 2.0], [3.0, 4.0]]}},
+    }
+    parameter_randomization_distributions = ParameterRandomizationSettings.structure(
+        parameter_randomization_dict, Dict[str, ParameterRandomizationSettings]
+    )
+    assert isinstance(parameter_randomization_distributions["mass"], UniformSettings)
+    assert isinstance(parameter_randomization_distributions["scale"], GaussianSettings)
+    assert isinstance(
+        parameter_randomization_distributions["length"], MultiRangeUniformSettings
+    )
+
+    # Check invalid distribution type
+    invalid_distribution_dict = {"mass": {"beta": {"alpha": 1.0, "beta": 2.0}}}
+    with pytest.raises(ValueError):
+        ParameterRandomizationSettings.structure(
+            invalid_distribution_dict, Dict[str, ParameterRandomizationSettings]
+        )
+
+    # Check min less than max in uniform
+    invalid_distribution_dict = {
+        "mass": {"uniform": {"min_value": 2.0, "max_value": 1.0}}
+    }
+    with pytest.raises(TrainerConfigError):
+        ParameterRandomizationSettings.structure(
+            invalid_distribution_dict, Dict[str, ParameterRandomizationSettings]
+        )
+
+    # Check min less than max in multirange
+    invalid_distribution_dict = {
+        "mass": {"multirangeuniform": {"intervals": [[2.0, 1.0]]}}
+    }
+    with pytest.raises(TrainerConfigError):
+        ParameterRandomizationSettings.structure(
+            invalid_distribution_dict, Dict[str, ParameterRandomizationSettings]
+        )
+
+    # Check multirange has valid intervals
+    invalid_distribution_dict = {
+        "mass": {"multirangeuniform": {"intervals": [[1.0, 2.0], [3.0]]}}
+    }
+    with pytest.raises(TrainerConfigError):
+        ParameterRandomizationSettings.structure(
+            invalid_distribution_dict, Dict[str, ParameterRandomizationSettings]
+        )
+
+    # Check non-Dict input
+    with pytest.raises(TrainerConfigError):
+        ParameterRandomizationSettings.structure(
+            "notadict", Dict[str, ParameterRandomizationSettings]
+        )

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

-import pytest
-
-from mlagents.trainers.sampler_class import SamplerManager
-from mlagents.trainers.sampler_class import (
-    UniformSampler,
-    MultiRangeUniformSampler,
-    GaussianSampler,
-)
-from mlagents.trainers.exception import TrainerError
-
-
-def sampler_config_1():
-    return {
-        "mass": {"sampler-type": "uniform", "min_value": 5, "max_value": 10},
-        "gravity": {
-            "sampler-type": "multirange_uniform",
-            "intervals": [[8, 11], [15, 20]],
-        },
-    }
-
-
-def check_value_in_intervals(val, intervals):
-    check_in_bounds = [a <= val <= b for a, b in intervals]
-    return any(check_in_bounds)
-
-
-def test_sampler_config_1():
-    config = sampler_config_1()
-    sampler = SamplerManager(config)
-
-    assert sampler.is_empty() is False
-    assert isinstance(sampler.samplers["mass"], UniformSampler)
-    assert isinstance(sampler.samplers["gravity"], MultiRangeUniformSampler)
-
-    cur_sample = sampler.sample_all()
-
-    # Check uniform sampler for mass
-    assert sampler.samplers["mass"].min_value == config["mass"]["min_value"]
-    assert sampler.samplers["mass"].max_value == config["mass"]["max_value"]
-    assert config["mass"]["min_value"] <= cur_sample["mass"]
-    assert config["mass"]["max_value"] >= cur_sample["mass"]
-
-    # Check multirange_uniform sampler for gravity
-    assert sampler.samplers["gravity"].intervals == config["gravity"]["intervals"]
-    assert check_value_in_intervals(
-        cur_sample["gravity"], sampler.samplers["gravity"].intervals
-    )
-
-
-def sampler_config_2():
-    return {"angle": {"sampler-type": "gaussian", "mean": 0, "st_dev": 1}}
-
-
-def test_sampler_config_2():
-    config = sampler_config_2()
-    sampler = SamplerManager(config)
-    assert sampler.is_empty() is False
-    assert isinstance(sampler.samplers["angle"], GaussianSampler)
-
-    # Check angle gaussian sampler
-    assert sampler.samplers["angle"].mean == config["angle"]["mean"]
-    assert sampler.samplers["angle"].st_dev == config["angle"]["st_dev"]
-
-
-def test_empty_samplers():
-    empty_sampler = SamplerManager({})
-    assert empty_sampler.is_empty()
-    empty_cur_sample = empty_sampler.sample_all()
-    assert empty_cur_sample == {}
-
-    none_sampler = SamplerManager(None)
-    assert none_sampler.is_empty()
-    none_cur_sample = none_sampler.sample_all()
-    assert none_cur_sample == {}
-
-
-def incorrect_uniform_sampler():
-    # Do not specify required arguments to uniform sampler
-    return {"mass": {"sampler-type": "uniform", "min-value": 10}}
-
-
-def incorrect_sampler_config():
-    # Do not specify 'sampler-type' key
-    return {"mass": {"min-value": 2, "max-value": 30}}
-
-
-def test_incorrect_uniform_sampler():
-    config = incorrect_uniform_sampler()
-    with pytest.raises(TrainerError):
-        SamplerManager(config)
-
-
-def test_incorrect_sampler():
-    config = incorrect_sampler_config()
-    with pytest.raises(TrainerError):
-        SamplerManager(config)