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import puzzle

def reward(self):
    increment = self.game.increment
    totalScore = self.game.totalScore
    # loose does not work yet
    #loose = self.game.loose
    return increment


    

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import random
import numpy as np
from collections import deque
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import RMSprop

class DQNAgent:
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size
        self.memory = deque(maxlen=100000)
        self.gamma = 0.9    # discount rate
        self.epsilon = 1.0  # exploration rate
        self.e_decay = .99
        self.e_min = 0.05
        self.learning_rate = 0.01
        self.model = self._build_model()

    def _build_model(self):
        # Neural Net for Deep-Q learning Model
        model = Sequential()
        model.add(Dense(20, input_dim=self.state_size, activation='tanh')) # Adds the first layer with 16 inputs
        model.add(Dense(20, activation='tanh', init='uniform')) # Adds Hidden layer with 20 nodes
        model.add(Dense(self.action_size, activation='linear')) # Adds output layer with 2 nodes
        model.compile(loss='mse',
                      optimizer=RMSprop(lr=self.learning_rate)) # Creates the model from all of the above
        return model

    def remember(self, state, action, reward, next_state, done):
        self.memory.append((state, action, reward, next_state, done))

    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return random.randrange(self.action_size)
        act_values = self.model.predict(state)
        return np.argmax(act_values[0])  # returns action

    def replay(self, batch_size):
        batch_size = min(batch_size, len(self.memory))
        minibatch = random.sample(self.memory, batch_size)
        X = np.zeros((batch_size, self.state_size))
        Y = np.zeros((batch_size, self.action_size))
        for i in range(batch_size):
            state, action, reward, next_state, done = minibatch[i]
            target = self.model.predict(state)[0]
            if done:
                target[action] = reward
            else:
                target[action] = reward + self.gamma * \
                            np.amax(self.model.predict(next_state)[0])
            X[i], Y[i] = state, target
        self.model.fit(X, Y, batch_size=batch_size, nb_epoch=1, verbose=0)
        if self.epsilon > self.e_min:
            self.epsilon *= self.e_decay

    def load(self, name):
        self.model.load_weights(name)

    def save(self, name):
        self.model.save_weights(name)


    

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