import tensorflow as tf

    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);
        with tf.variable_scope(scope+"_"+str(par_idx)):

            # state, target and action
