# -*- coding: utf-8 -*-

import random

import gym

import numpy as np

from collections import deque

from keras.models import Sequential

from keras.layers import Dense

from keras.optimizers import Adam

EPISODES = 1000

class DQNAgent:

    def __init__(self, state_size, action_size):

        self.state_size = state_size

        self.action_size = action_size

        self.memory = deque(maxlen=2000)

        self.gamma = 0.95    # discount rate

        self.epsilon = 1.0  # exploration rate

        self.epsilon_min = 0.01

        self.epsilon_decay = 0.995

        self.learning_rate = 0.001

        self.model = self._build_model()

    def _build_model(self):

        # Neural Net for Deep-Q learning Model

        model = Sequential()

        model.add(Dense(24, input_dim=self.state_size, activation='relu'))

        model.add(Dense(24, activation='relu'))

        model.add(Dense(self.action_size, activation='linear'))

        model.compile(loss='mse',

                      optimizer=Adam(lr=self.learning_rate))

        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):

        minibatch = random.sample(self.memory, batch_size)

        for state, action, reward, next_state, done in minibatch:

            target = reward

            if not done:

                target = (reward + self.gamma *

                          np.amax(self.model.predict(next_state)[0]))

            target_f = self.model.predict(state)

            print target_f

            print target

            target_f[0][action] = target

            self.model.fit(state, target_f, epochs=1, verbose=0)

        if self.epsilon > self.epsilon_min:

            self.epsilon *= self.epsilon_decay

    def load(self, name):

        self.model.load_weights(name)

    def save(self, name):

        self.model.save_weights(name)

if __name__ == "__main__":

    env = gym.make('CartPole-v1')

    state_size = env.observation_space.shape[0]

    action_size = env.action_space.n

    agent = DQNAgent(state_size, action_size)

    # agent.load("./save/cartpole-dqn.h5")

    done = False

    batch_size = 32

    for e in range(EPISODES):

        state = env.reset()

        state = np.reshape(state, [1, state_size])

        for time in range(500):

            # env.render()

            action = agent.act(state)

            next_state, reward, done, _ = env.step(action)

            reward = reward if not done else -10

            print 'reward:',reward

            next_state = np.reshape(next_state, [1, state_size])

            agent.remember(state, action, reward, next_state, done)

            state = next_state

            if done:

                print("episode: {}/{}, score: {}, e: {:.2}"

                      .format(e, EPISODES, time, agent.epsilon))

                break

        if len(agent.memory) > batch_size:

            agent.replay(batch_size)

        # if e % 10 == 0:

        #     agent.save("./save/cartpole-dqn.h5")