import tensorflow as tf


def featurize_state(state):
    scaled = scaler.transform([state])
    featurized = featurizer.transform(scaled)
    return featurized[0]

def build_policy_net_MountainCarContinuous(input_tf):
    mu = tf.layers.dense(input_tf, num_action, tf.nn.tanh, kernel_initializer=w_init, name='mu') # estimated action value
    sigma = tf.layers.dense(input_tf, num_action, tf.nn.softplus, kernel_initializer=w_init, name='sigma') # estimated variance
    return mu,sigma;

class PolicyEstimator_MountainCarContinuous():
    def __init__(self, entropy_beta=0.1, learning_rate=0.001, par_idx=0,scope="policy_estimator"):
        w_init = tf.random_normal_initializer(0.,.1);
