"""A value function (baseline) based on a GaussianMLP model."""
import numpy as np
from garage.np.baselines import Baseline
from garage.tf.regressors import GaussianMLPRegressor
[docs]class GaussianMLPBaseline(Baseline):
"""Gaussian MLP Baseline with Model.
It fits the input data to a gaussian distribution estimated by
a MLP.
Args:
env_spec (garage.envs.env_spec.EnvSpec): Environment specification.
subsample_factor (float): The factor to subsample the data. By
default it is 1.0, which means using all the data.
num_seq_inputs (float): Number of sequence per input. By default
it is 1.0, which means only one single sequence.
regressor_args (dict): Arguments for regressor.
name (str): Name of baseline.
"""
def __init__(
self,
env_spec,
subsample_factor=1.,
num_seq_inputs=1,
regressor_args=None,
name='GaussianMLPBaseline',
):
super().__init__(env_spec)
if regressor_args is None:
regressor_args = dict()
self._regressor = GaussianMLPRegressor(
input_shape=(env_spec.observation_space.flat_dim *
num_seq_inputs, ),
output_dim=1,
name=name,
subsample_factor=subsample_factor,
**regressor_args)
self.name = name
[docs] def fit(self, paths):
"""Fit regressor based on paths.
Args:
paths (list[dict]): Sample paths.
"""
observations = np.concatenate([p['observations'] for p in paths])
returns = np.concatenate([p['returns'] for p in paths])
self._regressor.fit(observations, returns.reshape((-1, 1)))
[docs] def predict(self, path):
"""Predict value based on paths.
Args:
path (list[dict]): Sample paths.
Returns:
numpy.ndarray: Predicted value.
"""
return self._regressor.predict(path['observations']).flatten()
[docs] def get_param_values(self):
"""Get parameter values.
Returns:
List[np.ndarray]: A list of values of each parameter.
"""
return self._regressor.get_param_values()
[docs] def set_param_values(self, flattened_params):
"""Set param values.
Args:
flattened_params (np.ndarray): A numpy array of parameter values.
"""
self._regressor.set_param_values(flattened_params)
[docs] def get_params_internal(self):
"""Get the params, which are the trainable variables.
Returns:
List[tf.Variable]: A list of trainable variables in the current
variable scope.
"""
return self._regressor.get_params_internal()