import numpy as np
import tensorflow as tf

# reproducible
np.random.seed(1)
tf.set_random_seed(1)


class PolicyGradient:
    def __init__(
            self,
            n_actions,
            n_features,
            learning_rate=0.01,
            reward_decay=0.95,
            output_graph=False,
    ):
        self.n_actions = n_actions
        self.n_features = n_features
        self.lr = learning_rate
        self.gamma = reward_decay

        self.ep_obs, self.ep_as, self.ep_rs = [], [], []

        self._build_net()

        self.sess = tf.Session()

        if output_graph:
            # $ tensorboard --logdir=logs
            # http://0.0.0.0:6006/
            # tf.train.SummaryWriter soon be deprecated, use following
            tf.summary.FileWriter("logs/", self.sess.graph)

        self.sess.run(tf.global_variables_initializer())
    
    def _build_net(self):
        with tf.name_scope('inputs'):
            self.tf_obs = tf.placeholder(tf.float32,[None,self.n_features],name='observation')
            self.tf_acts = tf.placeholder(tf.int32,[None,],name='actions_num')
            self.tf_vt = tf.placeholder(tf.float32,[None,],name='actions_value')

        #fc1
        layer = tf.layers.dense(
            inputs=self.tf_obs,
            units=10,
            activation=tf.nn.tanh,
            kernel_initializer=tf.random_normal_initializer(mean=0,stddev=0.3),
            bias_initializer=tf.constant_initializer(0,1),
            name='fc1'
        )

        #fc2
        all_act = tf.layers.dense(
            inputs=layer,
            units=self.n_actions,
            activation=None,
            kernel_initializer=tf.random_normal_initializer(mean=0,stddev=0.3),
            bias_initializer=tf.constant_initializer(0,1),
            name='fc2'
        )

        self.all_act_prob = tf.nn.softmax(all_act,name='act_prob')

        with tf.name_scope('loss'):
            neg_log_prob = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=all_act,labels=self.tf_acts)
            loss = tf.reduce_mean(neg_log_prob*self.tf_vt)

        with tf.name_scope('train'):
            self.train_op = tf.train.AdamOptimizer(self.lr).minimize(loss)
        
    def choose_action(self,observation):
        prob_weights = self.sess.run(self.all_act_prob,feed_dict={self.tf_obs:observation[np.newaxis,:]})
        action = np.random.choice(range(prob_weights.shape[1]),p=prob_weights.ravel())
        return action

    def store_transition(self,s,a,r):
        self.ep_obs.append(s)
        self.ep_as.append(a)
        self.ep_rs.append(r)

    def learn(self):
        discounted_ep_rs_norm = self._discount_and_norm_rewards()

        #train on episode
        self.sess.run(self.train_op,feed_dict={
            self.tf_obs:np.vstack(self.ep_obs),
            self.tf_acts:np.array(self.ep_as),
            self.tf_vt:discounted_ep_rs_norm})
        self.ep_obs,self.ep_as,self.ep_rs = [],[],[]
        return discounted_ep_rs_norm

    def _discount_and_norm_rewards(self):
        # discout the episode reward
        discounted_ep_rs = np.zeros_like(self.ep_rs)
        running_add = 0
        for t in reversed(range(0,len(self.ep_rs))):
            running_add = running_add * self.gamma + self.ep_rs[t]
            discounted_ep_rs[t] = running_add
        #normalize episode rewards
        discounted_ep_rs -= np.mean(discounted_ep_rs)
        discounted_ep_rs /= np.std(discounted_ep_rs)

        return discounted_ep_rs