import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import cv2
import gym
import matplotlib.pyplot as plt

class QNetwork(nn.Module):
    """Actor (Policy) Model."""

    def __init__(self, state_size, action_size, seed):
        """Initialize parameters and build model.
        Params
        ======
            state_size (int): Dimension of each state
            action_size (int): Dimension of each action
            seed (int): Random seed
        """
        super(QNetwork, self).__init__()
        self.seed = torch.manual_seed(seed)
        "*** YOUR CODE HERE ***"
        self.conv = nn.Sequential(
            nn.Conv2d(state_size[1], 32, kernel_size=8, stride=4),
            nn.ReLU(),
            nn.Conv2d(32, 64, kernel_size=4, stride=2),
            nn.ReLU(),
            nn.Conv2d(64, 64, kernel_size=3, stride=1),
            nn.ReLU()
            )

        self.fc = nn.Sequential(
            nn.Linear(64*7*7, 512),
            nn.ReLU(),
            nn.Linear(512, action_size)
        )

    def forward(self, state):
        """Build a network that maps state -> action values."""
        conv_out = self.conv(state).view(state.size()[0], -1)
        return self.fc(conv_out)


def pre_process(observation):
    """Process (210, 160, 3) picture into (1, 84, 84)"""
    x_t = cv2.cvtColor(cv2.resize(observation, (84, 84)), cv2.COLOR_BGR2GRAY)
    ret, x_t = cv2.threshold(x_t, 1, 255, cv2.THRESH_BINARY)
    return np.reshape(x_t, (1, 84, 84)), x_t


def stack_state(processed_obs):
    """Four frames as a state"""
    return np.stack((processed_obs, processed_obs, processed_obs, processed_obs), axis=0)


if __name__ == '__main__':
    env = gym.make('Breakout-v0')
    print('State shape: ', env.observation_space.shape)
    print('Number of actions: ', env.action_space.n)

    obs = env.reset()
    x_t, img = pre_process(obs)
    state = stack_state(img)
    print(np.shape(state[0]))

    # plt.imshow(img, cmap='gray')
    # 用cv2模块显示
    # cv2.imshow('Breakout', img)
    # cv2.waitKey(0)

    state = torch.randn(32, 4, 84, 84)  # (batch_size, color_channel, img_height,img_width)
    state_size = state.size()

    cnn_model = QNetwork(state_size, action_size=4, seed=1)
    outputs = cnn_model(state)
    print(outputs)

class ReplayBuffer:
    """Fixed-size buffer to store experience tuples."""

    def __init__(self, action_size, buffer_size, batch_size, seed):
        """Initialize a ReplayBuffer object.

        Params
        ======
            action_size (int): dimension of each action
            buffer_size (int): maximum size of buffer
            batch_size (int): size of each training batch
            seed (int): random seed
        """
        self.action_size = action_size
        self.memory = deque(maxlen=buffer_size)  
        self.batch_size = batch_size
        self.experience = namedtuple("Experience", field_names=["state", "action", "reward", "next_state", "done"])
        self.seed = random.seed(seed)
    
    def add(self, state, action, reward, next_state, done):
        """Add a new experience to memory."""
        e = self.experience(state, action, reward, next_state, done)
        self.memory.append(e)
    
    def sample(self):
        """Randomly sample a batch of experiences from memory."""
        experiences = random.sample(self.memory, k=self.batch_size)

        states = torch.from_numpy(np.stack([e.state for e in experiences if e is not None])).float().to(device)
        actions = torch.from_numpy(np.vstack([e.action for e in experiences if e is not None])).long().to(device)
        rewards = torch.from_numpy(np.vstack([e.reward for e in experiences if e is not None])).float().to(device)
        next_states = torch.from_numpy(np.stack([e.next_state for e in experiences if e is not None])).float().to(device)
        dones = torch.from_numpy(np.vstack([e.done for e in experiences if e is not None]).astype(np.uint8)).float().to(device)

        return (states, actions, rewards, next_states, dones)

    def __len__(self):
        """Return the current size of internal memory."""
        return len(self.memory)

import numpy as np
import random
from collections import namedtuple, deque

from q_model import QNetwork

import torch
import torch.nn.functional as F
import torch.optim as optim

BUFFER_SIZE = int(1e6)  # replay buffer size
BATCH_SIZE = 32         # minibatch size
GAMMA = 0.99            # discount factor
TAU = 1e-3              # for soft update of target parameters
LR = 1e-5               # learning rate
UPDATE_EVERY = 4        # how often to update the network

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)


class Agent():
    """Interacts with and learns from the environment."""

    def __init__(self, state_size, action_size, seed):
        """Initialize an Agent object.
        
        Params
        ======
            state_size (int): dimension of each state
            action_size (int): dimension of each action
            seed (int): random seed
        """
        self.state_size = state_size
        self.action_size = action_size
        self.seed = random.seed(seed)

        # Q-Network
        self.qnetwork_local = QNetwork(state_size, action_size, seed).to(device)  # behavior network
        self.qnetwork_target = QNetwork(state_size, action_size, seed).to(device)  # target network
        self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)

        # Replay memory
        self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
        # Initialize time step (for updating every UPDATE_EVERY steps)
        self.t_step = 0
    
    def step(self, state, action, reward, next_state, done):
        # Save experience in replay memory
        self.memory.add(state, action, reward, next_state, done)
        
        # Learn every UPDATE_EVERY time steps.
        self.t_step = (self.t_step + 1) % UPDATE_EVERY
        if self.t_step == 0:
            # If enough samples are available in memory, get random subset and learn
            if len(self.memory) > BATCH_SIZE:
                experiences = self.memory.sample()
                self.learn(experiences, GAMMA)

    def act(self, state, eps=0.):
        """Returns actions for given state as per current policy.
        
        Params
        ======
            state (array_like): current state
            eps (float): epsilon, for epsilon-greedy action selection
        """
        state = torch.from_numpy(state).float().unsqueeze(0).to(device)
        self.qnetwork_local.eval()
        with torch.no_grad():
            action_values = self.qnetwork_local(state)
        self.qnetwork_local.train()

        # Epsilon-greedy action selection
        if random.random() > eps:
            return np.argmax(action_values.cpu().data.numpy())
        else:
            return random.choice(np.arange(self.action_size))

    def learn(self, experiences, gamma):
        """Update value parameters using given batch of experience tuples.

        Params
        ======
            experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples 
            gamma (float): discount factor
        """
        states, actions, rewards, next_states, dones = experiences

        ## TODO: compute and minimize the loss
        # Get max predicted Q values (for next states) from target model
        Q_targets_next = self.qnetwork_target(next_states).detach().max(1)[0].unsqueeze(1)
        # Compute Q targets for current states
        Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))

        Q_expected = self.qnetwork_local(states).gather(1, actions)  # 固定行号，确认列号

        # Compute loss
        loss = F.mse_loss(Q_expected, Q_targets)
        # Minimize the loss
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

        # ------------------- update target network ------------------- #
        self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU)                     

    def soft_update(self, local_model, target_model, tau):
        """Soft update model parameters.
        θ_target = τ*θ_local + (1 - τ)*θ_target

        Params
        ======
            local_model (PyTorch model): weights will be copied from
            target_model (PyTorch model): weights will be copied to
            tau (float): interpolation parameter 
        """
        for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):
            target_param.data.copy_(tau*local_param.data + (1.0-tau)*target_param.data)

import gym
import random
import torch
import numpy as np
from collections import deque
from dqn_agent import Agent
import matplotlib.pyplot as plt
import cv2
import time


env = gym.make('Breakout-v0')
state_size = env.observation_space.shape
action_size = env.action_space.n
print('Original state shape: ', state_size)
print('Number of actions: ', env.action_space.n)

agent = Agent((32, 4, 84, 84), action_size, seed=1)  # state size (batch_size, 4 frames, img_height, img_width)
TRAIN = False  # train or test flag 


def pre_process(observation):
    """Process (210, 160, 3) picture into (1, 84, 84)"""
    x_t = cv2.cvtColor(cv2.resize(observation, (84, 84)), cv2.COLOR_BGR2GRAY)
    ret, x_t = cv2.threshold(x_t, 1, 255, cv2.THRESH_BINARY)
    return x_t


def init_state(processed_obs):
    return np.stack((processed_obs, processed_obs, processed_obs, processed_obs), axis=0)


def dqn(n_episodes=30000, max_t=40000, eps_start=1.0, eps_end=0.01, eps_decay=0.9995):
    """Deep Q-Learning.
    Params
    ======
        n_episodes (int): maximum number of training episodes
        max_t (int): maximum number of timesteps per episode, maximum frames
        eps_start (float): starting value of epsilon, for epsilon-greedy action selection
        eps_end (float): minimum value of epsilon
        eps_decay (float): multiplicative factor (per episode) for decreasing epsilon
    """
    scores = []  # list containing scores from each episode
    scores_window = deque(maxlen=100)  # last 100 scores
    eps = eps_start  # initialize epsilon
    for i_episode in range(1, n_episodes + 1):
        obs = env.reset()
        obs = pre_process(obs)
        state = init_state(obs)

        score = 0
        for t in range(max_t):
            action = agent.act(state, eps)
            next_state, reward, done, _ = env.step(action)
            # last three frames and current frame as the next state
            next_state = np.stack((state[1], state[2], state[3], pre_process(next_state)), axis=0)
            agent.step(state, action, reward, next_state, done)
            state = next_state
            score += reward
            if done:
                break
        scores_window.append(score)  # save most recent score
        scores.append(score)  # save most recent score
        eps = max(eps_end, eps_decay * eps)  # decrease epsilon
        print('\tEpsilon now : {:.2f}'.format(eps))
        print('\rEpisode {}\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_window)), end="")
        if i_episode % 1000 == 0:
            print('\rEpisode {}\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_window)))
            print('\rEpisode {}\tThe length of replay buffer now: {}'.format(i_episode, len(agent.memory)))

        if np.mean(scores_window) >= 50.0:
            print('\nEnvironment solved in {:d} episodes!\tAverage Score: {:.2f}'.format(i_episode - 100,
                                                                                         np.mean(scores_window)))
            torch.save(agent.qnetwork_local.state_dict(), 'checkpoint/dqn_checkpoint_solved.pth')
            break

    torch.save(agent.qnetwork_local.state_dict(), 'checkpoint/dqn_checkpoint_8.pth')
    return scores


if __name__ == '__main__':
    if TRAIN:
        start_time = time.time()
        scores = dqn()
        print('COST: {} min'.format((time.time() - start_time)/60))
        print("Max score:", np.max(scores))

        # plot the scores
        fig = plt.figure()
        ax = fig.add_subplot(111)
        plt.plot(np.arange(len(scores)), scores)
        plt.ylabel('Score')
        plt.xlabel('Episode #')
        plt.show()

    else:
        # load the weights from file
        agent.qnetwork_local.load_state_dict(torch.load('checkpoint/dqn_checkpoint_8.pth'))
        rewards = []
        for i in range(10):  # episodes, play ten times
            total_reward = 0
            obs = env.reset()
            obs = pre_process(obs)
            state = init_state(obs)
            for j in range(10000):  # frames, in case stuck in one frame
                action = agent.act(state)
                env.render()
                next_state, reward, done, _ = env.step(action)
                state = np.stack((state[1], state[2], state[3], pre_process(next_state)), axis=0)
                total_reward += reward

                # time.sleep(0.01)
                if done:
                    rewards.append(total_reward)
                    break

        print("Test rewards are:", *rewards)
        print("Average reward:", np.mean(rewards))
        env.close()