def main():
""" Main Program """
MODE = 'test'
SIM_SPEED = 1
save_first_winner = False
level_to_save = 0
level1 = build_blueprint([400, 450, 580, 100, BLACK],
multipliers=4,
xPos_interval=50)
level2 = build_blueprint([400, 400, 520, 100, BLACK],
multipliers=4,
xPos_interval=50)
level3 = build_blueprint([400, 350, 480, 100, BLACK],
multipliers=4,
xPos_interval=50)
level4 = build_blueprint([400, 250, 380, 100, BLACK],
multipliers=4,
xPos_interval=50)
level5 = build_blueprint([400, 150, 280, 100, BLACK],
multipliers=4,
xPos_interval=50)
level6 = build_blueprint([400, 50, 180, 100, BLACK],
multipliers=4,
xPos_interval=50)
levels = Block.generate(SCREEN_WIDTH, SCREEN_HEIGHT, 100, level1, level3,
level1, level4, level6)
#levels = Block.generate(SCREEN_WIDTH, SCREEN_HEIGHT, 100, level1)
agents = AgentManager(mode=MODE,
yPos_range=None,
starting_xPos=100,
starting_yPos=500,
xPos_range=None,
population_size=300,
screen_height=SCREEN_HEIGHT,
vertical_fuel_depletion_rate=0.0005,
horizontal_fuel_depletion_rate=0.0005,
screen_width=SCREEN_WIDTH,
load_agent_filepath='sim_data/default_best')
visual_system = VisualSystem.init(img_shape=(40, 40, 1),
latent_dims=3,
RE_delta=0.0,
model_folder='CNND',
start=6,
MODE=MODE,
preview_output=False)
memory_system = MemorySystem.init(MODE=MODE,
high_simularity_threshold=1.0e-08,
low_simularity_threshold=0.2,
forget_usage_threshold=0,
forget_age_threshold=50,
max_memory_size=50)
screen, clock, current_level, current_level_no = init(
SCREEN_WIDTH, SCREEN_HEIGHT, levels)
generation_count = 0
change_level_flag = False
adaption_complete_flag = False
winners = []
latent_representation = []
is_familiar = None
done = False
frame_count = 0
while not done:
# --- Event Processing ---
for event in pygame.event.get():
if event.type == pygame.QUIT:
done = True
if event.type == pygame.KEYDOWN:
if event.key == pygame.K_DOWN:
SIM_SPEED -= 10
elif event.key == pygame.K_UP:
SIM_SPEED += 10
elif event.key == pygame.K_s and MODE == 'train':
agents.save_best_agent()
for i in range(SIM_SPEED):
if MODE == 'train' or 'test':
# --- Update Agents ---
i = 0
while i < len(agents.not_sprites) and i > -1:
agent = agents.not_sprites[i]
if MODE == 'test' and visual_system is not None:
# ignore blackout screen at beginning
visual_system.process_image(screen,
agents,
frame_count,
preview_images=False)
is_familiar = visual_system.is_familiar()
if is_familiar == False and memory_system is not None:
if visual_system.supress == False:
# get latent representation from vs
latent_representation, _ = visual_system.generate_latent_representation(
)
memory, action = memory_system.query(
latent_representation)
if memory is not None and action == 'memory_to_fs_system_switch':
agents.temp_agent_store.append(agent)
agents.reset_temp_agent_store(
memory.get('solution'))
visual_system.supress = True
print(action)
elif memory is None and action == 'adaption_using_medium_memory_as_init_foundation':
pass
elif memory is None and action == 'adaption_using_low_memory_and_random_init_foundation':
MODE = 'adapt'
agents.adaptive_rand_population_init(
mode=MODE,
rand_init_population_size=400,
rand_to_current_model_split=(1.0, 0.0),
rand_init_mutation_rate=0.0,
current_model_mutation_rate=0.99
) # leave one model the current one unchanges
print('WHERRRRR')
else:
agents.restore_original_test_agent()
# print('visual system is frame familiar: %s' % is_familiar)
agent.think(current_level, SCREEN_WIDTH, SCREEN_HEIGHT)
agent.update(SCREEN_HEIGHT)
if agent.current_closest_block.hit(
agent) or agent.off_screen(
SCREEN_HEIGHT,
SCREEN_WIDTH) or agent.fuel_depleted():
agent.computeFitness()
agents.splice(i)
i -= 1
if len(winners) == 0:
if agent.rect.right > current_level[len(
current_level) - 1].top_block.rect.right - 10:
if save_first_winner == True and level_to_save == current_level_no and MODE == 'train':
agents.save_best_agent()
save_first_winner = False
change_level_flag = True
agent.fuel = 1.0
agent.rect.x = agents.starting_xPos
agent.rect.y = agents.starting_yPos
agent.velocity_x = 0
agent.velocity_y = 0
agent.name = 'winner' + str(i)
winners.append(agent)
agents.splice(i, mode='delete')
i -= 1
else:
if agent.rect.right > current_level[len(
current_level
) - 1].top_block.rect.left and agent.rect.right < current_level[
len(current_level) - 1].top_block.rect.right:
change_level_flag = True
agent.fuel = 1.0
agent.rect.x = agents.starting_xPos
agent.rect.y = agents.starting_yPos
agent.velocity_x = 0
agent.velocity_y = 0
agent.name = 'winner' + str(i)
winners.append(agent)
agents.splice(i, mode='delete')
i -= 1
i += 1
if change_level_flag:
if MODE != 'adapt':
agents.splice(index=None, mode='all')
agents.not_sprites = winners
change_level_flag = False
winners = []
if current_level_no < len(levels) - 1:
current_level_no += 1
else:
current_level_no = 0
current_level = levels[current_level_no]
if visual_system is not None and visual_system.supress == True:
visual_system.supress = False
break
else:
solution = get_best_winner(winners)
memory_system.create_memory(
latent_representation=latent_representation,
solution=solution.functional_system,
tag='mem_level' + str(current_level_no))
agents.reset_temp_agent_store(
solution.functional_system)
visual_system.supress = True
winners = []
agents.dead_agents = []
MODE = 'test'
print('ADAPTION COMPLETE: USING NEW SOLUTION')
# check if all active agents are dead, the perform GA and reset game level and epochs
if len(agents.not_sprites) == 0:
if MODE == 'test':
dead_agent = agents.dead_agents[0]
dead_agent.reset()
new_population = [dead_agent]
agents.update_arrays(input=new_population)
else:
if MODE == 'adapt':
a = 0
new_population = GenticAlgorithm.produceNextGeneration(
population=agents.dead_agents,
screen_width=SCREEN_WIDTH,
screen_height=SCREEN_HEIGHT,
agent_meta_data=agents.__dict__)
agents.update_arrays(new_population)
generation_count += 1
print(
'generation = %s population size = %s level no = %s / %s'
% (generation_count, len(new_population),
current_level_no, len(levels)))
if MODE != 'adapt':
current_level_no = 0
current_level = levels[current_level_no]
# --- Drawing ---
screen.fill(WHITE)
for block in current_level:
block.draw(screen)
agents.draw(screen, mode=MODE)
capture_success = capture(surface=screen,
mode=MODE,
remove_blank_frames=True,
level_number=current_level_no,
save_folder_path='test',
preview=True)
if MODE == 'capture' and capture_success:
if current_level_no < len(levels) - 1:
current_level_no += 1
current_level = levels[current_level_no]
else:
done = True
pygame.display.flip()
clock.tick(60)
frame_count += 1
if change_level_flag:
change_level_flag = False
if MODE == 'adapt':
print('Mode: %s is familiar: %s model in use: %s ' %
(MODE, is_familiar, 'Finding solution'))
elif MODE == 'test':
print('Mode: %s is familiar: %s model in use: %s ' %
(MODE, is_familiar,
agents.not_sprites[0].functional_system.name))
pygame.quit()
print('SIMULATION LEVEL COMPLETED BY AGENT')
