import tensorflow as tf

"""
For Pendulum-v0
"""
class PolicyEstimator_Pendulum():
    def __init__(self, entropy_beta=0.01, learning_rate=0.01, 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
            self.state = tf.placeholder(tf.float32, [None,num_state], name="state")
            self.target = tf.placeholder(tf.float32,[None,1], name="target")
            self.a_his = tf.placeholder(tf.float32, [None, num_action], name="action_hist")        
            
            # layers
            l_a = tf.layers.dense(self.state, 200, tf.nn.relu6, kernel_initializer=w_init, name='la')
            self.mu = tf.layers.dense(l_a, num_action, tf.nn.tanh, kernel_initializer=w_init, name='mu') # estimated action value
            self.sigma = tf.layers.dense(l_a, num_action, tf.nn.softplus, kernel_initializer=w_init, name='sigma') # estimated variance
            
