Replace continous mlp baseline w model primitive

src/garage/tf/baselines/__init__.py

@@ -1,15 +1,12 @@
 """Baseline estimators for TensorFlow-based algorithms."""
 from garage.tf.baselines.continuous_mlp_baseline import ContinuousMLPBaseline
-from garage.tf.baselines.continuous_mlp_baseline_with_model import (
-    ContinuousMLPBaselineWithModel)
 from garage.tf.baselines.gaussian_cnn_baseline_with_model import (
     GaussianCNNBaselineWithModel)
 from garage.tf.baselines.gaussian_conv_baseline import GaussianConvBaseline
 from garage.tf.baselines.gaussian_mlp_baseline import GaussianMLPBaseline
 
 __all__ = [
     'ContinuousMLPBaseline',
-    'ContinuousMLPBaselineWithModel',
     'GaussianConvBaseline',
     'GaussianCNNBaselineWithModel',
     'GaussianMLPBaseline',

src/garage/tf/baselines/continuous_mlp_baseline.py

@@ -1,15 +1,13 @@
-"""This module implements continuous mlp baseline."""
+"""A value function (baseline) based on a MLP model."""
 import numpy as np
 
-from garage.core import Serializable
 from garage.misc.overrides import overrides
 from garage.np.baselines import Baseline
-from garage.tf.core import Parameterized
 from garage.tf.regressors import ContinuousMLPRegressor
 
 
-class ContinuousMLPBaseline(Baseline, Parameterized, Serializable):
-    """A value function using a mlp network."""
+class ContinuousMLPBaseline(Baseline):
+    """A value function using a MLP network."""
 
     def __init__(
             self,
@@ -20,37 +18,31 @@ def __init__(
             name='ContinuousMLPBaseline',
     ):
         """
-        Constructor.
+        Continuous MLP Baseline.
 
-        :param env_spec: environment specification.
-        :param subsample_factor:
-        :param num_seq_inputs: number of sequence inputs.
-        :param regressor_args: regressor arguments.
+        It fits the input data by performing linear regression
+        to the outputs.
+
+        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.
         """
-        Parameterized.__init__(self)
-        Serializable.quick_init(self, locals())
-        super(ContinuousMLPBaseline, self).__init__(env_spec)
+        super().__init__(env_spec)
         if regressor_args is None:
             regressor_args = dict()
 
         self._regressor = ContinuousMLPRegressor(
-            input_shape=(
-                env_spec.observation_space.flat_dim * num_seq_inputs, ),
+            input_shape=(env_spec.observation_space.flat_dim *
+                         num_seq_inputs, ),
             output_dim=1,
             name=name,
             **regressor_args)
         self.name = name
 
-    @overrides
-    def get_param_values(self, **tags):
-        """Get parameter values."""
-        return self._regressor.get_param_values(**tags)
-
-    @overrides
-    def set_param_values(self, val, **tags):
-        """Set parameter values to val."""
-        self._regressor.set_param_values(val, **tags)
-
     @overrides
     def fit(self, paths):
         """Fit regressor based on paths."""
@@ -63,6 +55,17 @@ def predict(self, path):
         """Predict value based on paths."""
         return self._regressor.predict(path['observations']).flatten()
 
+    @overrides
+    def get_param_values(self, **tags):
+        """Get parameter values."""
+        return self._regressor.get_param_values(**tags)
+
+    @overrides
+    def set_param_values(self, flattened_params, **tags):
+        """Set parameter values to val."""
+        self._regressor.set_param_values(flattened_params, **tags)
+
     @overrides
     def get_params_internal(self, **tags):
+        """Get internal parameters."""
         return self._regressor.get_params_internal(**tags)

src/garage/tf/regressors/__init__.py

@@ -11,8 +11,6 @@
     CategoricalMLPRegressorWithModel)
 from garage.tf.regressors.continuous_mlp_regressor import (
     ContinuousMLPRegressor)
-from garage.tf.regressors.continuous_mlp_regressor_with_model import (
-    ContinuousMLPRegressorWithModel)
 from garage.tf.regressors.gaussian_cnn_regressor_model import (
     GaussianCNNRegressorModel)
 from garage.tf.regressors.gaussian_cnn_regressor_with_model import (
@@ -23,8 +21,8 @@
 __all__ = [
     'BernoulliMLPRegressor', 'BernoulliMLPRegressorWithModel',
     'CategoricalMLPRegressor', 'CategoricalMLPRegressorWithModel',
-    'ContinuousMLPRegressor', 'ContinuousMLPRegressorWithModel',
-    'GaussianCNNRegressorModel', 'GaussianCNNRegressorWithModel',
-    'GaussianMLPRegressor', 'GaussianConvRegressor', 'Regressor2',
-    'StochasticRegressor', 'StochasticRegressor2'
+    'ContinuousMLPRegressor', 'GaussianCNNRegressorModel',
+    'GaussianCNNRegressorWithModel', 'GaussianMLPRegressor',
+    'GaussianConvRegressor', 'Regressor2', 'StochasticRegressor',
+    'StochasticRegressor2'
 ]