Source code for

"""A linear value function (baseline) based on features."""
import numpy as np

from import Baseline

[docs]class LinearFeatureBaseline(Baseline): """A linear value function (baseline) based on features. Args: env_spec (garage.envs.env_spec.EnvSpec): Environment specification. reg_coeff (float): Regularization coefficient. name (str): Name of baseline. """ def __init__(self, env_spec, reg_coeff=1e-5, name='LinearFeatureBaseline'): del env_spec self._coeffs = None self._reg_coeff = reg_coeff = name self.lower_bound = -10 self.upper_bound = 10
[docs] def get_param_values(self): """Get parameter values. Returns: List[np.ndarray]: A list of values of each parameter. """ return self._coeffs
[docs] def set_param_values(self, flattened_params): """Set param values. Args: flattened_params (np.ndarray): A numpy array of parameter values. """ self._coeffs = flattened_params
def _features(self, path): """Extract features from path. Args: path (list[dict]): Sample paths. Returns: numpy.ndarray: Extracted features. """ obs = np.clip(path['observations'], self.lower_bound, self.upper_bound) length = len(path['observations']) al = np.arange(length).reshape(-1, 1) / 100.0 return np.concatenate( [obs, obs**2, al, al**2, al**3, np.ones((length, 1))], axis=1) # pylint: disable=unsubscriptable-object
[docs] def fit(self, paths): """Fit regressor based on paths. Args: paths (list[dict]): Sample paths. """ featmat = np.concatenate([self._features(path) for path in paths]) returns = np.concatenate([path['returns'] for path in paths]) reg_coeff = self._reg_coeff for _ in range(5): self._coeffs = np.linalg.lstsq( + reg_coeff * np.identity(featmat.shape[1]),, rcond=-1)[0] if not np.any(np.isnan(self._coeffs)): break reg_coeff *= 10
[docs] def predict(self, paths): """Predict value based on paths. Args: paths (list[dict]): Sample paths. Returns: numpy.ndarray: Predicted value. """ if self._coeffs is None: return np.zeros(len(paths['observations'])) return self._features(paths).dot(self._coeffs)