def main():
parser = ArgumentParser()
parser.add_argument("map_file")
parser.add_argument("ai_in")
parser.add_argument("ai_out")
parser.add_argument("--override", action="store_true")
args = parser.parse_args()
if os.path.isfile(args.ai_out) and not args.override:
print(f"Cannot override output {args.out_file}")
exit()
with open(args.map_file, "rb") as fp:
M = pickle.load(fp)
car = Car()
car.C_drift_control = 0.3
car.C_drift_traction = 0.4
car.C_drift_sideslip = 0.3
# car.F_engine_max = 10
car.F_engine_max = 8
# sensor = Sensor(300, total=16)
sensor = NonLinearSensor(300, total=16)
env = Environment(car, sensor, M)
train_dqn(env, args)
env.on_exit()
def _get_action_from_user_input(env: Environment):
try:
input_integer = int(input(">> Enter bet size: $"))
except ValueError:
print("Value must be an integer", file=sys.stderr)
return _get_action_from_user_input(env)
if input_integer in env.get_legal_actions():
return input_integer
else:
print("Cannot bet that much", file=sys.stderr)
return _get_action_from_user_input(env)
def main():
parser = ArgumentParser()
parser.add_argument("map_file")
parser.add_argument("ai_in")
parser.add_argument("ai_out")
parser.add_argument("--override", action="store_true")
args = parser.parse_args()
if os.path.isfile(args.ai_out) and not args.override:
print(f"Cannot override output {args.out_file}")
exit()
with open(args.map_file, "rb") as fp:
M = pickle.load(fp)
car = Car()
sensor = Sensor(170)
env = Environment(car, sensor, M)
train_agent(env, args)
env.on_exit()
def main():
parser = ArgumentParser()
parser.add_argument("map_file")
args = parser.parse_args()
with open(args.map_file, "rb") as fp:
M = pickle.load(fp)
pygame.init()
screen = pygame.display.set_mode([1500, 900])
running = True
car = Car()
player_controls = PlayerControls()
# sensor = Sensor(200, total=16)
sensor = NonLinearSensor(300, total=16)
env = Environment(car, sensor, M)
env_render = EnvironmentRenderer()
while running:
for ev in pygame.event.get():
if ev.type == pygame.QUIT:
running = False
elif player_controls.on_pygame_event(ev):
continue
elif ev.type == pygame.KEYDOWN:
if ev.key == pygame.K_r:
env.reset()
elif ev.key == pygame.K_s:
env_render.show_sensor = not env_render.show_sensor
elif ev.key == pygame.K_d:
car.drift = not car.drift
print(f"drift={car.drift}")
screen.fill((255, 255, 255))
action = player_controls.get_action()
observation, reward, done, info = env.step(action,
reset_finished=False)
env_render.render(screen, env)
if reward != 0:
print(f"reward: {reward}")
pygame.display.flip()
pygame.quit()
env.on_exit()
def main():
parser = argparse.ArgumentParser()
parser.add_argument("map_file")
args = parser.parse_args()
with open(args.map_file, "rb") as fp:
M = pickle.load(fp)
car = Car()
sensor = Sensor(200, total=16)
env = Environment(car, sensor, M)
last_frame = time.perf_counter()
total_reads = 0
avg_tps = 0
action = [1, 0, 0]
try:
while True:
env.step(action, 1)
curr_frame = time.perf_counter()
dt = curr_frame - last_frame
tps = 1 / dt
print(f"tps: {int(tps):5d}\r", end='')
last_frame = curr_frame
if total_reads == 0:
avg_tps = tps
else:
N = total_reads
avg_tps = (avg_tps * N + tps) / (N + 1)
total_reads += 1
except KeyboardInterrupt:
pass
finally:
print(f"\navg tps: {int(avg_tps)}")
env.on_exit()
def act(self, env: Environment):
env.render(mode="human")
bet_size = _get_action_from_user_input(env)
return bet_size
env.render(mode="human")
bet_size = _get_action_from_user_input(env)
return bet_size
def _get_action_from_user_input(env: Environment):
try:
input_integer = int(input(">> Enter bet size: $"))
except ValueError:
print("Value must be an integer", file=sys.stderr)
return _get_action_from_user_input(env)
if input_integer in env.get_legal_actions():
return input_integer
else:
print("Cannot bet that much", file=sys.stderr)
return _get_action_from_user_input(env)
if __name__ == "__main__":
env = Environment()
user_agent = UserAgent()
finished = False
while not finished:
action = user_agent.act(env)
print("\n\nFlipping coin...")
state, reward, finished, debug_info = env.step(action)
if reward > 0:
print("WON :)\n")
else:
print("LOST :(\n")
def main():
parser = ArgumentParser()
parser.add_argument("map_file")
parser.add_argument("ai_file")
parser.add_argument("--drift", action="store_true")
args = parser.parse_args()
with open(args.map_file, "rb") as fp:
M = pickle.load(fp)
car = Car()
car.drift = args.drift
car.C_drift_control = 0.3
car.C_drift_traction = 0.4
car.C_drift_sideslip = 0.3
car.F_engine_max = 10
# sensor = Sensor(300, total=16)
sensor = NonLinearSensor(300, total=16)
env = Environment(car, sensor, M)
env = EnvironmentWrapper(dqn_controls, env)
from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten
model = create_dqn_model(env)
print(model.summary())
# model.load_weights("dqn_weights.h5f")
# model.load_weights("dqn_drift_weights.h5f")
model.load_weights(args.ai_file)
pygame.init()
screen = pygame.display.set_mode([1500, 900])
env_render = EnvironmentRenderer()
running = True
curr_tick = 0
action = None
while running:
for ev in pygame.event.get():
if ev.type == pygame.QUIT:
running = False
elif ev.type == pygame.KEYDOWN:
if ev.key == pygame.K_r:
curr_tick = 0
env.reset()
elif ev.key == pygame.K_s:
env_render.show_sensor = not env_render.show_sensor
elif ev.key == pygame.K_d:
car.drift = not car.drift
print(f"drift={car.drift}")
screen.fill((255, 255, 255))
if curr_tick % 1 == 0 or action is None:
observation = env.get_observation()
observation = np.array(observation).reshape(
(1, 1, env.nb_observations))
action = model.predict(observation)
action = np.argmax(action[0])
_, reward, _, _ = env.step(action, reset_finished=False, dt=1)
curr_tick += 1
env_render.render(screen, env)
if reward != 0:
print(f"reward: {reward}")
pygame.display.flip()
pygame.quit()
env.on_exit()