import numpy as np 
import tensorflow as tf 

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

class DoubleDQN:
    def __init__(
        self,
        n_actions,
        n_features,
        learning_rate=0.005,
        reward_decay=0.9,
        e_greedy=0.9,
        replace_target_iter=200,
        memory_size=3000,
        batch_size=32,
        e_greedy_increment=None,
        output_graph=True,
        double_q = True,
        sess = None):

        self.n_actions = n_actions 
        self.n_features = n_features
        self.lr = learning_rate
        self.gamma = reward_decay
        self.epsilon_max = e_greedy
        self.replace_target_iter = replace_target_iter
        self.memory_size = memory_size
        self.batch_size = batch_size
        self.epsilon_increment = e_greedy_increment
        self.epsilon = 0 if e_greedy_increment is not None else self.epsilon_max

        self.double_q= double_q


        #total learning step
        self.learn_step_counter = 0

        #init memory
        self.memory = np.zeros((self.memory_size,n_features*2+2))

        self._build_net()

        t_params = tf.get_collection('target_net_params')
        e_params = tf.get_collection('eval_net_params')

        
        self.target_replace_op = [ tf.assign(t,e) for t,e in zip(t_params,e_params)]
        
        if sess is None:

            self.sess = tf.Session()
            self.sess.run(tf.global_variables_initializer())
        else:
            self.sess = sess

        

        if output_graph:
            tf.summary.FileWriter('log/',self.sess.graph)
        
        self.cost_his = []


    def _build_net(self):
        def build_layers(s, c_names, n_l1, w_initializer, b_initializer):
            with tf.variable_scope('l1'):
                w1 = tf.get_variable('w1', [self.n_features, n_l1], initializer=w_initializer, collections=c_names)
                b1 = tf.get_variable('b1', [1, n_l1], initializer=b_initializer, collections=c_names)
                l1 = tf.nn.relu(tf.matmul(s, w1) + b1)

            with tf.variable_scope('l2'):
                w2 = tf.get_variable('w2', [n_l1, self.n_actions], initializer=w_initializer, collections=c_names)
                b2 = tf.get_variable('b2', [1, self.n_actions], initializer=b_initializer, collections=c_names)
                out = tf.matmul(l1, w2) + b2
            return out

        # ------------------ build evaluate_net ------------------
        self.s = tf.placeholder(tf.float32, [None, self.n_features], name='s')  # input
        self.q_target = tf.placeholder(tf.float32, [None, self.n_actions], name='Q_target')  # for calculating loss

        with tf.variable_scope('eval_net'):
            c_names, n_l1, w_initializer, b_initializer = \
                ['eval_net_params', tf.GraphKeys.GLOBAL_VARIABLES], 20, \
                tf.random_normal_initializer(0., 0.3), tf.constant_initializer(0.1)  # config of layers

            self.q_eval = build_layers(self.s, c_names, n_l1, w_initializer, b_initializer)

        with tf.variable_scope('loss'):
            self.loss = tf.reduce_mean(tf.squared_difference(self.q_target, self.q_eval))
        with tf.variable_scope('train'):
            self._train_op = tf.train.RMSPropOptimizer(self.lr).minimize(self.loss)

        # ------------------ build target_net ------------------
        self.s_ = tf.placeholder(tf.float32, [None, self.n_features], name='s_')    # input
        with tf.variable_scope('target_net'):
            c_names = ['target_net_params', tf.GraphKeys.GLOBAL_VARIABLES]

            self.q_next = build_layers(self.s_, c_names, n_l1, w_initializer, b_initializer)
       

    def store_transition(self,s,a,r,s_):
        if not hasattr(self,'memory_counter'):
            self.memory_counter = 0
        transition = np.hstack((s,[a,r],s_))
        index = self.memory_counter % self.memory_size
        self.memory[index,:] = transition
        self.memory_counter += 1
    
    def choose_action(self,observation):
        observation = observation[np.newaxis,:]
      
        action_value = self.sess.run(self.q_eval,feed_dict={self.s:observation})
        action = np.argmax(action_value)

        if not hasattr(self,'q'):
            self.q = []
            self.running_q = 0
        self.running_q = self.running_q*0.99 + 0.01 * np.max(action_value)
        self.q.append(self.running_q)

        if np.random.uniform()>self.epsilon:
            action = np.random.randint(0,self.n_actions)
        
        return action
    
    def learn(self):
        if self.learn_step_counter % self.replace_target_iter == 0:
            self.sess.run(self.target_replace_op)
            print('\ntarget_params_replaced\n')

        if self.memory_counter > self.memory_size:
            sample_index = np.random.choice(self.memory_size,size=self.batch_size)
        else:
            sample_index = np.random.choice(self.memory_counter,size=self.batch_size)
        batch_memory = self.memory[sample_index,:]

        q_next,q_eval4next = self.sess.run(
            [self.q_next,self.q_eval],
            feed_dict={self.s_:batch_memory[:,-self.n_features:],
            self.s:batch_memory[:,-self.n_features:]}
        )

        q_eval = self.sess.run(self.q_eval,{self.s:batch_memory[:,:self.n_features]})

        q_target = q_eval.copy()

        batch_index = np.arange(self.batch_size,dtype=np.int32)
        eval_act_index = batch_memory[:,self.n_features].astype(int)
        reward = batch_memory[:,self.n_features+1]

        if self.double_q:
            max_act4next = np.argmax(q_eval4next,axis=1)
            selected_q_next = q_next[batch_index,max_act4next]
        else:
            selected_q_next = np.max(q_next,axis=1)
        
        q_target[batch_index,eval_act_index] = reward + self.gamma * selected_q_next

        _,self.cost = self.sess.run(
            [self._train_op,self.loss],
            feed_dict={
                self.s:batch_memory[:,:self.n_features],
                self.q_target:q_target
            }
        )

        self.cost_his.append(self.cost)

        self.epsilon = self.epsilon + self.epsilon_increment if self.epsilon < self.epsilon_max else self.epsilon_max

        self.learn_step_counter += 1

