Unity-Technologies/ml-agents · error · TrainerConfigError

Sampler configuration does not contain sampler_parameters :

Error message

Sampler configuration does not contain sampler_parameters : {d}.

What it means

The parameter randomization structure hook requires both `sampler_type` and `sampler_parameters` keys; after validating sampler_type it checks for `sampler_parameters` and raises this error if absent. The sampler_parameters value is then strictly converted into the concrete sampler settings class.

Source

Thrown at ml-agents/mlagents/trainers/settings.py:301

        d: Union[Mapping, float], t: type
    ) -> "ParameterRandomizationSettings":
        """
        Helper method to a ParameterRandomizationSettings class. Meant to be registered with
        cattr.register_structure_hook() and called with cattr.structure(). This is needed to handle
        the special Enum selection of ParameterRandomizationSettings classes.
        """
        if isinstance(d, (float, int)):
            return ConstantSettings(value=d)
        if not isinstance(d, Mapping):
            raise TrainerConfigError(
                f"Unsupported parameter randomization configuration {d}."
            )
        if "sampler_type" not in d:
            raise TrainerConfigError(
                f"Sampler configuration does not contain sampler_type : {d}."
            )
        if "sampler_parameters" not in d:
            raise TrainerConfigError(
                f"Sampler configuration does not contain sampler_parameters : {d}."
            )
        enum_key = ParameterRandomizationType(d["sampler_type"])
        t = enum_key.to_settings()
        return strict_to_cls(d["sampler_parameters"], t)

    @staticmethod
    def unstructure(d: "ParameterRandomizationSettings") -> Mapping:
        """
        Helper method to a ParameterRandomizationSettings class. Meant to be registered with
        cattr.register_unstructure_hook() and called with cattr.unstructure().
        """
        _reversed_mapping = {
            UniformSettings: ParameterRandomizationType.UNIFORM,
            GaussianSettings: ParameterRandomizationType.GAUSSIAN,
            MultiRangeUniformSettings: ParameterRandomizationType.MULTIRANGEUNIFORM,
            ConstantSettings: ParameterRandomizationType.CONSTANT,
        }

View on GitHub (pinned to 3ecb446f75)

Solutions

1. Add a `sampler_parameters` mapping with the required fields (e.g. min_value, max_value for uniform).
2. Verify indentation so sampler_parameters nests under the parameter name.
3. Compare against a working example config from the ML-Agents repo.

Example fix

# before
my_param:
  sampler_type: uniform

# after
my_param:
  sampler_type: uniform
  sampler_parameters:
    min_value: 1.0
    max_value: 5.0

for name, spec in cfg.get('environment_parameters', {}).items():
    if isinstance(spec, dict) and 'sampler_parameters' not in spec:
        raise ValueError(f"environment_parameters[{name}] missing 'sampler_parameters'")

def has_sampler_parameters(spec) -> bool:
    return isinstance(spec, dict) and isinstance(spec.get('sampler_parameters'), dict)

try:
    settings = TrainerSettings.structure(raw)
except TrainerConfigError as e:
    if 'sampler_parameters' in str(e):
        print('Add sampler_parameters with min_value/max_value etc.')
    raise

When it happens

Trigger: Writing `environment_parameters: {my_param: {sampler_type: uniform}}` without the sibling `sampler_parameters` mapping.

Common situations: Copying only the sampler_type line from docs, or deleting sampler_parameters when editing values, leaving an incomplete sampler block.

Understand the failure class

Background: "missing required argument" and "the following required arguments were not provided": what required-argument errors mean and how to fix them — this error's family across 20 libraries.

Related errors

• Sampler configuration does not contain sampler_type : {d}.
• Unsupported parameter randomization configuration {d}.
• There was an error decoding Config file from {config_path}.
• Error parsing yaml file. Please check for formatting errors.
• Unsupported reward signal configuration {d}.

AI-assisted analysis of Unity-Technologies/ml-agents@3ecb446f75 (2026-09-02). Data as JSON: /api/errors/07c12f7911da3709. Report an issue: GitHub.