I have code that generates enemy movements, and a neural network that learns to hit the enemy, it takes into account the speed of the bullet, predicts the position of the enemy when the bullet ends its flight, and so on.

but the problem is that I can't manually generate player movements, bullet types, and even ballistics. what advice can you give when developing new features, and what code can you write me for this? it would be nice to have visualization to simplify development, it should be in real time, because I will integrate the trained neural network into the android emulator into the game. Or are other methods needed? write code if needed.

import numpy as np
import random
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# Constants
GAME_AREA_WIDTH = 1000
GAME_AREA_HEIGHT = 1000

# Random enemy movement
def update_enemy_position(enemy_pos, enemy_vel):
    new_pos_x = enemy_pos[0] + enemy_vel[0]
    new_pos_y = enemy_pos[1] + enemy_vel[1]
    return new_pos_x, new_pos_y

def random_velocity():
    speed = random.uniform(1, 40)
    angle = random.uniform(0, 2 * np.pi)
    vel_x = speed * np.cos(angle)
    vel_y = speed * np.sin(angle)
    return vel_x, vel_y

# Neural network
input_neurons = 9 # Including the enemy velocities (2 additional inputs)
output_neurons = 2

model = Sequential()
model.add(Dense(32, activation='relu', input_dim=input_neurons))
model.add(Dense(64, activation='relu'))
model.add(Dense(output_neurons))

model.compile(optimizer='adam', loss='mse', metrics=['accuracy'])

# Input: bullet speed, bullet range, player x, player y, enemy x, enemy y, enemy velocity x, enemy velocity y
def desired_joystick_coords(player_pos, enemy_pos, enemy_vel, bullet_speed, bullet_range):
    time_to_hit = bullet_range / bullet_speed
    
    future_enemy_pos = enemy_pos
    for _ in range(int(time_to_hit)):
        future_enemy_pos = update_enemy_position(future_enemy_pos, enemy_vel)
        enemy_vel = random_velocity() # Update enemy velocity to create unpredictable movements

    joystick_x = future_enemy_pos[0] - player_pos[0]
    joystick_y = future_enemy_pos[1] - player_pos[1]
    return joystick_x, joystick_y

def generate_training_data(num_samples):
    training_data = []

    for _ in range(num_samples):
        bullet_speed = random.uniform(500, 10000)
        bullet_range = random.uniform(2000, 20000)
        player_pos = (random.randint(0, GAME_AREA_WIDTH), random.randint(0, GAME_AREA_HEIGHT))
        enemy_pos = (random.randint(0, GAME_AREA_WIDTH), random.randint(0, GAME_AREA_HEIGHT))
        enemy_vel = random_velocity()
        step = random.randint(0, 100)

        desired_coords = desired_joystick_coords(player_pos, enemy_pos, enemy_vel, bullet_speed, bullet_range)
        input_data = (bullet_speed, bullet_range, player_pos[0], player_pos[1], enemy_pos[0], enemy_pos[1], enemy_vel[0], enemy_vel[1], step)
        output_data = desired_coords
        training_data.append((input_data, output_data))
    
    return training_data

# Generate and prepare training data
num_samples = 100000
raw_training_data = generate_training_data(num_samples)
X, y = zip(*raw_training_data)

# Train the neural network
model.fit(np.array(X), np.array(y), epochs=100, validation_split=0.2)
model.save('my_model4.h5')