class_docstr: A linear value function (baseline) based on features.
  
  Args:
      env_spec (garage.envs.env_spec.EnvSpec): Environment specification.
      reg_coeff (float): Regularization coefficient.
      features (list[str]): Name of features.
      name (str): Name of baseline.
class_docstr_tok: ["A","linear","value","function","(","baseline",")","based","on","features",".","Args",":","env_spec","(","garage.envs.env_spec.EnvSpec",")",":","Environment","specification",".","reg_coeff","(","float",")",":","Regularization","coefficient",".","features","(","list","[","str","]",")",":","Name","of","features",".","name","(","str",")",":","Name","of","baseline","."]
code: def __init__(
      self,
      env_spec,
      features=None,
      reg_coeff=1e-5,
      name="LinearMultiFeatureBaseline",
  ):
      super().__init__(env_spec, reg_coeff, name)
      features = features or ["observations"]
      self._feature_names = features
  
imports: import numpy
  from garage.np.baselines import LinearFeatureBaseline
label_desc: Use image node_id 1 to create a new LinearMultiFeatureBaseline object from inherited base classes: LinearFeatureBaseline with example: obj = LinearMultiFeatureBaseline(env_spec, features, reg_coeff, name)