# Double DQN 練習
1. DQN概念說明:
DQN就是Deep Learning與Q_Learning的結合，傳統Q_Learning所建置的Q表會因為環境的多種狀態與動作而過於龐大運算上也較消耗機器效能，所以我們藉由深度學習的方式來預測我們的下一步，也可以結合各種不同的神經網路來進行訓練。

2. 何為DDQN:
DDQN與DQN最大的差別就是分為實際進行訓練的evaluation network與訓練目標 target network，而target network久久更新一次，evaluation network則持續更新向target network逼近。
## 程式實作部分
* #### 匯入所需函式與參數設定

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import gym

# Hyper Parameters
BATCH_SIZE = 32
LR = 0.01                   # learning rate
EPSILON = 0.9               # greedy policy
GAMMA = 0.9                 # reward discount
TARGET_REPLACE_ITER = 100   # target update frequency
MEMORY_CAPACITY = 2000
env = gym.make('CartPole-v0')
env = env.unwrapped
N_ACTIONS = env.action_space.n
N_STATES = env.observation_space.shape[0]

* #### 建立神經網路

class Net(nn.Module):
    def __init__(self, ):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(N_STATES, 50)
        self.fc1.weight.data.normal_(0, 0.1)   # initialization
        self.out = nn.Linear(50, N_ACTIONS)
        self.out.weight.data.normal_(0, 0.1)   # initialization

    def forward(self, x):
        x = self.fc1(x)
        x = F.relu(x)
        actions_value = self.out(x)
        return actions_value

* #### 建置DQN

class DQN(object):
    def __init__(self):
        self.eval_net, self.target_net = Net(), Net()
        self.learn_step_counter = 0                                     # for target updating
        self.memory_counter = 0                                         # for storing memory
        self.memory = np.zeros((MEMORY_CAPACITY, N_STATES * 2 + 2))     # initialize memory
        self.optimizer = torch.optim.Adam(self.eval_net.parameters(), lr=LR)
        self.loss_func = nn.MSELoss()

    def choose_action(self, x):
        x = torch.unsqueeze(torch.FloatTensor(x), 0)
        # input only one sample
        if np.random.uniform() < EPSILON:   # greedy
            actions_value = self.eval_net.forward(x)

            action = torch.max(actions_value, 1)[1].data.numpy()
           
            action = action[0] if ENV_A_SHAPE == 0 else action.reshape(ENV_A_SHAPE)  # return the argmax index
        else:   # random
            action = np.random.randint(0, N_ACTIONS)
            action = action if ENV_A_SHAPE == 0 else action.reshape(ENV_A_SHAPE)
        return action

    def store_transition(self, s, a, r, s_):
        transition = np.hstack((s, [a, r], s_))
        # replace the old memory with new memory
        index = self.memory_counter % MEMORY_CAPACITY
        self.memory[index, :] = transition
        self.memory_counter += 1

    def learn(self):
        # target parameter update
        if self.learn_step_counter % TARGET_REPLACE_ITER == 0:
            self.target_net.load_state_dict(self.eval_net.state_dict())
        self.learn_step_counter += 1
        
        # sample batch transitions
        sample_index = np.random.choice(MEMORY_CAPACITY, BATCH_SIZE)
        b_memory = self.memory[sample_index, :]
        
        
        b_s = torch.FloatTensor(b_memory[:, :N_STATES])       
        b_a = torch.LongTensor(b_memory[:, N_STATES:N_STATES+1].astype(int))
        b_r = torch.FloatTensor(b_memory[:, N_STATES+1:N_STATES+2])
        b_s_ = torch.FloatTensor(b_memory[:, -N_STATES:])
        
        # q_eval w.r.t the action in experience
        q_eval = self.eval_net(b_s).gather(1, b_a)  # shape (batch, 1)
        
        q_next = self.target_net(b_s_).detach()     # detach from graph, don't backpropagate
        q_target = b_r + GAMMA * q_next.max(1)[0].view(BATCH_SIZE, 1)   # shape (batch, 1)
        loss = self.loss_func(q_eval, q_target)

        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

* #### 最終環境訓練與結果

dqn = DQN()


print('\nCollecting experience...')
for i_episode in range(250):
    s = env.reset()
    ep_r = 0
    while True:
        env.render()
        a = dqn.choose_action(s)

        # take action
        s_, r, done, info = env.step(a)

        # modify the reward
        x, x_dot, theta, theta_dot = s_
        r1 = (env.x_threshold - abs(x)) / env.x_threshold - 0.8
        r2 = (env.theta_threshold_radians - abs(theta)) / env.theta_threshold_radians - 0.5
        r = r1 + r2
        dqn.store_transition(s, a, r, s_)

        ep_r += r
        if dqn.memory_counter > MEMORY_CAPACITY:
            dqn.learn()
            if done:
                print('Ep: ', i_episode,
                      '| Ep_r: ', round(ep_r, 2))

        if done:
            break
        s = s_
env.close()