score, done = 0.0, False
# Loop until terminal state
while not done:
clock.tick(FPS) # generate new frame
gameDisplay.fill(WHITE)
env.render(gameDisplay)
a = qvalue_network.best_actions( np.expand_dims(s[0],axis=0), np.expand_dims(s[1], axis=0) ).ravel()
action_index = np.argmax( a )
s, reward, done = env.step(action_index)
if (reward != 0.0):
score += reward
font = pygame.font.SysFont(None, 18)
text = font.render("Score: %.2f" % score, True, BLACK)
gameDisplay.blit(text,(DISPLAY_SHAPE[0]/3,60))
# Update Display
pygame.display.update()
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit()
quit()
ep_reward = 0
terminal = False
num_steps = 0
l = 1.0
while not terminal:
# Epsilon Greedy
if np.random.random() < EPSILON_ALPHA * np.exp(-EPSILON_BETA * num_steps):
a = generate_random_action()
else:
a = qvalue_network.best_actions( np.expand_dims(s[0],axis=0), np.expand_dims(s[1], axis=0) ).ravel()
# Collect environment data
s2, r, terminal = env.step( np.argmax(a) )
# Add data to ExperienceReplay memory
if UPDATE_REPLAY:
if np.abs(r) > 0.0:
er.add_experience(s, a, r, terminal, s2)
else:
if np.random.random() < 0.0018:
er.add_experience(s, a, r, terminal, s2)
# Keep adding experience to the memory until
# there are at least minibatch size samples
if er.size() > MINIBATCH_SIZE:
s_batch, a_batch, r_batch, t_batch, s2_batch = er.get_batch(MINIBATCH_SIZE)