请写出文献中算法的完整python代码

由于提供的文献主要描述了算法的设计和实验结果，并没有提供具体的Python代码实现，因此无法直接从文献中提取完整的Python代码。不过，我可以帮助您根据文献中的描述，编写一个大致的框架代码。这个框架代码将包括状态空间定义、动作空间定义、奖励函数设计以及训练过程的主要步骤。

以下是一个基于文献描述的简化版Python代码示例：

import numpy as np
import tensorflow as tf
from collections import deque
import random

class USVEnvironment:
    def __init__(self):
        self.state_size = 10  # Example state size
        self.action_size = 11  # Discrete action space
        self.reset()

    def reset(self):
        # Reset the environment to initial state
        self.os_position = np.array([10, 20])
        self.os_heading = 0
        self.ts_positions = [np.array([20, 5]), np.array([20, 35])]
        self.ts_headings = [45, -45]
        self.nif = self.calculate_nif()
        self.done = False
        return self.get_state()

    def step(self, action):
        # Apply action and get new state, reward, done
        self.update_os(action)
        self.update_ts()
        self.nif = self.calculate_nif()
        reward = self.calculate_reward()
        self.done = self.check_collision() or self.reached_target()
        return self.get_state(), reward, self.done

    def update_os(self, action):
        # Update own ship's position and heading based on action
        delta_heading = action * 5  # Example action scaling
        self.os_heading += delta_heading
        self.os_position += np.array([np.cos(np.radians(self.os_heading)), np.sin(np.radians(self.os_heading))]) * 2.6

    def update_ts(self):
        # Update target ship positions
        for i, ts_heading in enumerate(self.ts_headings):
            self.ts_positions[i] += np.array([np.cos(np.radians(ts_heading)), np.sin(np.radians(ts_heading))]) * 1.8

    def calculate_nif(self):
        # Calculate Navigation Impact Factor (NIF)
        nifs = []
        for ts_pos, ts_heading in zip(self.ts_positions, self.ts_headings):
            distance = np.linalg.norm(self.os_position - ts_pos)
            bearing_angle = np.arctan2(ts_pos[1] - self.os_position[1], ts_pos[0] - self.os_position[0])
            bearing_angle = np.degrees(bearing_angle) % 360
            nif = self.fuzzy_inference(distance, bearing_angle)
            nifs.append(nif)
        return nifs

    def fuzzy_inference(self, distance, bearing_angle):
        # Simplified fuzzy inference for NIF
        if distance < 7:
            if 5 <= bearing_angle <= 112.5:
                return 1.0  # High NIF
            elif 112.5 < bearing_angle <= 180:
                return 0.5  # Medium NIF
            else:
                return 0.0  # Low NIF
        else:
            return 0.0

    def calculate_reward(self):
        # Calculate reward based on NIF, COLREGs, etc.
        r_vo = self.dynamic_obstacle_avoidance_reward()
        r_g = self.target_reward()
        r_c = self.boundary_reward()
        r_da = self.dynamic_approach_reward()
        r_sa = self.static_approach_reward()
        r_t = self.turning_reward()
        r_clgs = self.colregs_reward()
        return r_vo + r_g + r_c + r_da + r_sa + r_t + r_clgs

    def dynamic_obstacle_avoidance_reward(self):
        # Example VO-based reward
        if any(nif > 0.5 for nif in self.nif):
            return -1.0
        else:
            return 0.0

    def target_reward(self):
        # Example target reward
        target_position = np.array([80, 45])
        distance_to_target = np.linalg.norm(self.os_position - target_position)
        if distance_to_target < 2:
            return 1.0
        else:
            return 0.0

    def boundary_reward(self):
        # Example boundary reward
        if 0 <= self.os_position[0] <= 90 and 0 <= self.os_position[1] <= 40:
            return 0.0
        else:
            return -1.0

    def dynamic_approach_reward(self):
        # Example dynamic approach reward
        return 0.0

    def static_approach_reward(self):
        # Example static approach reward
        return 0.0

    def turning_reward(self):
        # Example turning reward
        return -abs(self.os_heading / 180)

    def colregs_reward(self):
        # Example COLREGs reward
        for nif, ts_pos, ts_heading in zip(self.nif, self.ts_positions, self.ts_headings):
            if nif > 0.5:
                bearing_angle = np.arctan2(ts_pos[1] - self.os_position[1], ts_pos[0] - self.os_position[0])
                bearing_angle = np.degrees(bearing_angle) % 360
                if 5 <= bearing_angle <= 112.5:
                    return -1.0
        return 0.0

    def check_collision(self):
        # Check for collision
        for ts_pos in self.ts_positions:
            if np.linalg.norm(self.os_position - ts_pos) < 2:
                return True
        return False

    def reached_target(self):
        # Check if target is reached
        target_position = np.array([80, 45])
        distance_to_target = np.linalg.norm(self.os_position - target_position)
        return distance_to_target < 2

    def get_state(self):
        # Get the current state
        state = np.concatenate([self.os_position, [self.os_heading], self.nif])
        return state

class DuelingDQN(tf.keras.Model):
    def __init__(self, state_size, action_size):
        super(DuelingDQN, self).__init__()
        self.dense1 = tf.keras.layers.Dense(128, activation='relu')
        self.dense2 = tf.keras.layers.Dense(128, activation='relu')
        self.value = tf.keras.layers.Dense(1)
        self.advantage = tf.keras.layers.Dense(action_size)

    def call(self, state):
        x = self.dense1(state)
        x = self.dense2(x)
        value = self.value(x)
        advantage = self.advantage(x)
        q_values = value + (advantage - tf.reduce_mean(advantage, axis=1, keepdims=True))
        return q_values

def train_dqn(env, episodes=40000, max_steps=100, batch_size=1024, gamma=0.95, epsilon=0.995, epsilon_decay=0.00005):
    state_size = env.state_size
    action_size = env.action_size
    replay_buffer = deque(maxlen=1000000)
    dqn = DuelingDQN(state_size, action_size)
    target_dqn = DuelingDQN(state_size, action_size)
    optimizer = tf.keras.optimizers.Adam(lr=0.0003)
    
    for episode in range(episodes):
        state = env.reset()
        total_reward = 0
        for step in range(max_steps):
            if np.random.rand() < epsilon:
                action = np.random.choice(action_size)
            else:
                q_values = dqn(tf.expand_dims(state, axis=0))
                action = tf.argmax(q_values[0]).numpy()
            
            next_state, reward, done = env.step(action)
            replay_buffer.append((state, action, reward, next_state, done))
            state = next_state
            total_reward += reward
            
            if len(replay_buffer) > batch_size:
                minibatch = random.sample(replay_buffer, batch_size)
                states, actions, rewards, next_states, dones = zip(*minibatch)
                
                states = np.array(states)
                actions = np.array(actions)
                rewards = np.array(rewards)
                next_states = np.array(next_states)
                dones = np.array(dones)
                
                target_q_values = target_dqn(next_states).numpy()
                max_q_values = np.max(target_q_values, axis=1)
                targets = rewards + gamma * max_q_values * (1 - dones)
                
                with tf.GradientTape() as tape:
                    q_values = dqn(states)
                    action_indices = tf.stack([tf.range(batch_size), actions], axis=1)
                    chosen_q_values = tf.gather_nd(q_values, action_indices)
                    loss = tf.reduce_mean(tf.square(targets - chosen_q_values))
                
                gradients = tape.gradient(loss, dqn.trainable_variables)
                optimizer.apply_gradients(zip(gradients, dqn.trainable_variables))
                
                if step % 10 == 0:
                    target_dqn.set_weights(dqn.get_weights())
        
            if done:
                break
        
        epsilon = max(epsilon * (1 - epsilon_decay), 0.01)
        print(f"Episode {episode}, Total Reward: {total_reward}")

if __name__ == "__main__":
    env = USVEnvironment()
    train_dqn(env)

### 说明
1. **环境类 (`USVEnvironment`)**: 定义了环境的状态、动作、奖励等方法。
2. **深度神经网络模型 (`DuelingDQN`)**: 使用Keras构建了一个双流DQN模型。
3. **训练函数 (`train_dqn`)**: 实现了DQN的训练过程，包括经验回放池、ε-贪婪策略、目标网络更新等。

请注意，这只是一个简化版的实现，实际应用中可能需要更复杂的环境建模、更多的超参数调优以及更详细的奖励函数设计。希望这个示例能为您提供一个良好的起点。