Hi, I've implemented the following Evader Code. I want you to go deeper in my code, understand fully and after Come with coding standards to update my code to the industry standards . Later I'll provide the code for DQN do the same and also fix the matmul error it raises. Here is my Evader code: 
import numpy as np
import random
import math

import rospy
import tf
import time
from rospy.rostime import Time
from sensor_msgs.msg import LaserScan
from ackermann_msgs.msg import AckermannDrive
from nav_msgs.msg import Odometry
from DQN import DQNAgentPytorch
from reset import reset_car_position

FORWARD = 0
LEFT = 1
RIGHT = 2
REVERSE = 3
ACTION_SPACE = [FORWARD, LEFT, RIGHT, REVERSE]

class EvaderNode:
    def __init__(self, agent):
        rospy.init_node('evader_node', anonymous=True)

        rospy.Subscriber('car_1/scan', LaserScan, self.scan_callback)
        rospy.Subscriber('car_1/base/odom', Odometry, self.odom_callback)

        self.drive_pub = rospy.Publisher('car_1/command', AckermannDrive, queue_size=20)

        self.init_location = None
        self.current_location = None
        self.goal = (10, 10)

        self.obstacle_penalty = -250
        self.destination_reward = 500
        self.distance_threshold = 0.5
        self.collision_threshold = 0.2 

        self.agent = agent

        self.is_training = True
        
        self.init_odom_received = False
        self.latest_odom_msg = None
        self.reached_destination = False
        
        self.reward = 0
        self.angle_range = [(-3 * np.pi / 4, -np.pi / 4), (np.pi / 4, 3 * np.pi / 4)]

        self.max_steps_per_episode = 100
        self.current_episode = 0
        self.max_episodes = 10000
    
    def process_scan(self, scan):
        state = np.array(scan.ranges)[::10]
        return state


    def check_collision(self, scan):

        front_ranges = []
        rear_ranges = []

        for i, angle in enumerate(np.arange(len(scan.ranges)) * scan.angle_increment + scan.angle_min):
            if self.angle_range[0][0] <= angle <= self.angle_range[0][1]:
                front_ranges.append(scan.ranges[i])
            elif self.angle_range[1][0] <= angle <= self.angle_range[1][1]:
                rear_ranges.append(scan.ranges[i])

        # Check for obstacles in the front section
        min_front_range = min(front_ranges)
        if min_front_range < self.collision_threshold:
            # Obstacle detected in the front
            return True
        else:
            # Check for obstacles in the rear section
            min_rear_range = min(rear_ranges)
            
            if min_rear_range < self.collision_threshold:
                # Obstacle detected in the rear
                return True

        return False 


    def action_to_drive_msg(self, action):
        drive_msg = AckermannDrive()
        if action == FORWARD:
            drive_msg.speed = 2.0
            drive_msg.steering_angle = 0.0
        elif action == LEFT:
            drive_msg.speed = 2.0
            drive_msg.steering_angle = float(random.uniform(math.pi / 4, math.pi / 2))
        elif action == RIGHT:
            drive_msg.speed = 2.0
            drive_msg.steering_angle = float(random.uniform(-math.pi / 2, -math.pi / 4))
        elif action == REVERSE:
            drive_msg.speed = -2.0
            drive_msg.steering_angle = 0.0

        return drive_msg

    def calculate_reward(self, collision_detected, done):
        reward = 0
        if done:
            reward += self.destination_reward
            return reward
        if collision_detected:
            reward += self.obstacle_penalty
            return reward
        
        current_dist_diff = np.linalg.norm(np.array(self.goal) - np.array(self.current_location)[:2])
        init_dist_diff = np.linalg.norm(np.array(self.goal) - np.array(self.init_location)[:2])

        if current_dist_diff < init_dist_diff:
            reward += 100
        else:
            reward -= 100
        return reward

    def update_reward(self, collision_detected, done):
        self.reward += self.calculate_reward(collision_detected, done)


    def scan_callback(self, scan):
        state = self.process_scan(scan)

        scan_timestamp = scan.header.stamp
        odom_timestamp = self.latest_odom_msg.header.stamp
        
        print(f'Scan Timestamp: {scan_timestamp.to_sec()} Odometry Message Timestamp: {odom_timestamp.to_sec()}')
        
        time_diff = scan_timestamp - odom_timestamp
        time_diff_sec = time_diff.to_sec()
        
        # Adjust the time threshold as per your requirement
        time_threshold = 10

        if abs(time_diff_sec) > time_threshold:  # Adjust the threshold as per your requirement
            print("LaserScan and Odometry messages are not in sync!")
            return
        else:
            print('LaserScan and Odometry messages are in sync!')    
        
        while self.current_episode <= self.max_episodes:
            distance_to_goal = np.linalg.norm(np.array(self.goal) - np.array(self.current_location)[:2])

            print('Distance to reach goal:', distance_to_goal)
            
            # if distance_to_goal >= 20:
            #     self.reward = 0
            #     reset_car_position()
            
            state = np.concatenate((state, [self.current_location[2]], [distance_to_goal]))
            
            action = self.agent.choose_action(state, test=not self.is_training)
            collision_detected = self.check_collision(scan)

            if collision_detected and not self.reached_destination:
                print('Collision Detected')

            drive_msg = self.action_to_drive_msg(action)
            
            print(f'Action chosen: {action}, Yaw: {drive_msg.steering_angle}')
            
            self.drive_pub.publish(drive_msg)

            done = False

            # if self.current_episode >self.max_episodes:


            if self.is_training :
                print("Current Episode: ", self.current_episode, 'Episode Left:',self.max_episodes -self.current_episode)
                self.agent.add_experience(state, action, self.reward, state, done)
                self.agent.train(32) # Set the batch size here

                self.agent.update_epsilon(self.agent.epsilon * 0.995)

                if distance_to_goal < self.distance_threshold:
                    done = True
                    drive_msg.speed = 0.0
                    self.drive_pub.publish(drive_msg)
                    print("----Goal Reached----")
                    # reset_car_position()
                    self.reward = 0
                    rospy.sleep(1)
                self.current_episode +=1

            # else:
            #     reset_car_position()
            #     self.agent.load_weights('model_weights.pth')
            #     action = self.agent.choose_action(state, test=True)
            #     print(action)
            
            print(f"Reward before taking the action {action}: {self.reward}")

            self.update_reward(collision_detected, done)

            print(f"Reward after taking the action {action}: {self.reward}")
        
        # self.current_episode = 0
        # self.agent.save_weights('model_weights.pth')
        # self.is_training = False
                # rospy.signal_shutdown('Exit condition met')
                # return

    def update_current_location(self, odom_msg):
        position = odom_msg.pose.pose.position
        orientation = odom_msg.pose.pose.orientation
        euler_angles = tf.transformations.euler_from_quaternion(
            (orientation.x, orientation.y, orientation.z, orientation.w))

        self.current_location = (position.x, position.y, euler_angles[2])  # (x, y, yaw)

        print("Current Location of the Car:", self.current_location[:2])

    def odom_callback(self, odom_msg):
        if not self.init_odom_received:
            self.init_location = (odom_msg.pose.pose.position.x, odom_msg.pose.pose.position.y)
            print(f"Initial Location of the Car: {self.init_location}")
            self.init_odom_received = True

        self.update_current_location(odom_msg)
        self.latest_odom_msg = odom_msg
        

if __name__ == '__main__':
    try:
        action_space = ACTION_SPACE
        observation_space = np.zeros((111,))  # Based on the number of data points in the lidar scan plus additional state variables

        agent = DQNAgentPytorch(action_space, observation_space)
        node = EvaderNode(agent)

        while not node.init_odom_received:
            rospy.sleep(0.1)
        
        rospy.spin()
        # node.run(10) # Change the number of episodes here
        

    except rospy.ROSInterruptException:
        pass