# -*- coding: utf-8 -*-
# https5) ://keon.io/deep-q-learning/


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

import matplotlib.pyplot as plt

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)
            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')
    # env = gym.wrappers.Monitor(env, '/home/nlafarge/Projects/gym/examples/sandbox/.save/',
    #                            video_callable=lambda episode_id: episode_id % 100 == 0, force=True)
    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

    score_list = []
    average_list = []
    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
            next_state = np.reshape(next_state, [1, state_size])
            agent.remember(state, action, reward, next_state, done)
            state = next_state
            if done:
                score_list.append(time)
                average_list.append(np.mean(score_list))
                print("episode: {}/{}, score: {}, e: {:.2}"
                    .format(e, EPISODES, time, agent.epsilon))
                break
        if len(agent.memory) > batch_size:
            agent.replay(batch_size)
        # if e % 100 == 0:
        #      agent.save("/home/nlafarge/Projects/gym/examples/sandbox/.save/cartpole-dqn-{}.h5".format(e))

    x_vals = range(len(score_list))
    import numpy as np
    coefs = np.polyfit(x_vals, score_list, 1)
    # ffit = np.polyval(x_vals, coefs[::-1])
    ffit = np.poly1d(coefs)

    trials = ['Trial {}: {}'.format(i, s) for i, s in enumerate(score_list) if i % 100 == 0]
    for t in trials:
        print t

    plt.figure(1)
    plt.scatter(x_vals, score_list, c='b')
    # plt.plot(x_vals, ffit(x_vals), c='r', ls='dashed')
    plt.plot(x_vals, average_list, c='r')
    plt.xlabel('Trial Number')
    plt.ylabel('Balance Time')
    plt.title('CartPole DQN Trial Results')
    plt.legend(['Trials', 'Average'])
    from matplotlib.ticker import MaxNLocator

    ax = plt.figure().gca()
    ax.xaxis.set_major_locator(MaxNLocator(integer=True))

    plt.show(block=False)
    #
    # plt.figure(2)
    # plt.plot(average_list)
    # plt.xlabel('Trial Number')
    # plt.ylabel('Average Balance Time')
    # plt.title('CartPole DQN Average Trial Results')
    # ax = plt.figure().gca()
    # ax.xaxis.set_major_locator(MaxNLocator(integer=True))
    #
    # plt.show(block=False)