File size: 1,992 Bytes
fe446ed | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | import os
import numpy as np
import pygame
import tensorflow as tf
from .fyp_simulation import GameEnv
from .fyp_simulation.ddqn_keras import DDQNAgent
# model_path = "ddqn_model.keras"
model_path = os.path.join(os.path.dirname(__file__), 'fyp_simulation', 'ddqn_model.keras')
try:
model = tf.keras.models.load_model(model_path)
print("Model loaded successfully!")
except Exception as e:
print(f" Failed to load model: {e}")
TOTAL_GAMETIME = 10000
N_EPISODES = 10000
REPLACE_TARGET = 10
game = GameEnv.RacingEnv()
game.fps = 60
ddqn_agent = DDQNAgent(alpha=0.0005, gamma=0.99, n_actions=5, epsilon=0.02,
epsilon_end=0.01, epsilon_dec=0.999, replace_target=REPLACE_TARGET,
batch_size=64, input_dims=19, fname=model_path)
ddqn_agent.load_model()
ddqn_agent.update_network_parameters()
def run():
for e in range(N_EPISODES):
game.reset()
done = False
score = 0
counter = 0
gtime = 0
observation_, reward, done = game.step(0)
observation = np.array(observation_)
while not done:
for event in pygame.event.get():
if event.type == pygame.QUIT:
return
action = ddqn_agent.choose_action(observation)
observation_, reward, done = game.step(action)
observation_ = np.array(observation_)
if reward == 0:
counter += 1
if counter > 100:
done = True
else:
counter = 0
score += reward
observation = observation_
gtime += 1
if gtime >= TOTAL_GAMETIME:
done = True
game.render(action)
# if score > best_score:
# best_score = score
# ddqn_agent.save_model()
# print(f" New best score {best_score}! Model saved.")
print(f"Episode: {e}, Reward: {score}")
run() |