could this be sorted out and adapted to play a game? [closed] - conv-neural-network

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i asked chatgpt to design a fps bot and this is what it showed me
i was talking to it about the system architecture related to predicting and interpreting data in an interface so it can use it to interact, and it designed a system it says can be used to machine learn video games
import numpy as np
import tensorflow as tf
# Define the game environment and its parameters
game_board_height = 100
game_board_width = 100
num_channels = 3
num_possible_moves = 5
game_state = "playing"
game_board = np.zeros((game_board_height, game_board_width, num_channels))
# Define the CNN model architecture
model = tf.keras.Sequential([
tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(game_board_height, game_board_width, num_channels)),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(num_possible_moves)
])
# Define the optimizer and loss function
optimizer = tf.keras.optimizers.Adam()
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
# Define the rewards system
def get_reward(game_state):
if game_state == "win":
return 100
elif game_state == "lose":
return -100
else:
return 0
# Define the discount factor
discount_factor = 0.95
# Define the exploration rate
exploration_rate = 1.0
# Define the training loop
while game_state == "playing":
# Choose an action based on the current state and the exploration rate
if np.random.rand() < exploration_rate:
action = np.random.randint(num_possible_moves)
else:
prediction = model.predict(np.array([game_board]))
action = np.argmax(prediction)
# Make the chosen move and get the new state and reward
new_game_board, new_game_state = make_move(game_board, action)
reward = get_reward(new_game_state)
# Calculate the TD target
q_values = model.predict(np.array([game_board]))
td_target = reward + discount_factor * np.max(q_values)
# Calculate the TD error and update the model
td_error = td_target - q_values[0, action]
q_values[0, action] += 0.1 * td_error
model.fit(np.array([game_board]), q_values, epochs=1, verbose=0)
# Update the game state and board
game_state = new_game_state
game_board = new_game_board
# Update the exploration rate
exploration_rate *= 0.99
exploration_rate = max(0.1, exploration_rate)
# Use the trained model to make the next move in the game
prediction = model.predict(np.array([game_board]))
next_move = np.argmax(prediction)
# Make the next move in the game
game_board, game_state = make_move(game_board, next_move)
##
##
import cv2
import numpy as np
import tensorflow as tf
# Define the game environment and its parameters
game_board_height = 100
game_board_width = 100
num_channels = 3
num_possible_moves = 5
game_state = "playing"
game_board = np.zeros((game_board_height, game_board_width, num_channels))
# Define the CNN model architecture
model = tf.keras.Sequential([
tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(game_board_height, game_board_width, num_channels)),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(num_possible_moves)
])
# Define the optimizer and loss function
optimizer = tf.keras.optimizers.Adam()
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
# Define the rewards system
def get_reward(game_state):
if game_state == "win":
return 100
elif game_state == "lose":
return -100
else:
return 0
# Define the discount factor
discount_factor = 0.95
# Define the exploration rate
exploration_rate = 1.0
# Define the training loop
while game_state == "playing":
# Choose an action based on the current state and the exploration rate
if np.random.rand() < exploration_rate:
action = np.random.randint(num_possible_moves)
else:
prediction = model.predict(np.array([game_board]))
action = np.argmax(prediction)
# Find the nearest enemy and aim at it
game_board_rgb = cv2.cvtColor(game_board.astype(np.uint8), cv2.COLOR_BGR2RGB)
gray = cv2.cvtColor(game_board_rgb, cv2.COLOR_RGB2GRAY)
enemy_template = cv2.imread('enemy_template.png', cv2.IMREAD_GRAYSCALE)
result = cv2.matchTemplate(gray, enemy_template, cv2.TM_CCOEFF_NORMED)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
enemy_x, enemy_y = max_loc[0] + enemy_template.shape[1] // 2, max_loc[1] + enemy_template.shape[0] // 2
aim_x, aim_y = game_board_width // 2, game_board_height // 2
if enemy_x < aim_x:
aim_x -= 1
elif enemy_x > aim_x:
aim_x += 1
if enemy_y < aim_y:
aim_y -= 1
elif enemy_y > aim_y:
aim_y += 1
action += 1 # Aim action is the next possible action after the movement actions
aim_move = (aim_x, aim_y)
# Make the chosen move and get the new state and reward
new_game_board, new_game_state = make_move(game_board, action)
reward = get_reward(new_game_state)
# Calculate the TD target
i wanted to use cnns to play games