plot_map(self.map, self.obs, display)
for images in self._agent_images:
for surf, rectangle in images:
display.blit(surf, rectangle)
display.blit(self._info_surface, (0, 0), None, pygame.BLEND_RGB_SUB)
self._info_surface.fill(black) # clear notifications from previous round
pygame.display.update()
if __name__ == "__main__":
from cars.physics import SimplePhysics
from cars.track import generate_map
np.random.seed(3)
random.seed(3)
m = generate_map(8, 5, 3, 3)
SimpleCarWorld(1, m, SimplePhysics, SimpleCarAgent, timedelta=0.2).run()
# если вы хотите продолжить обучение уже существующей модели, вместо того,
# чтобы создавать новый мир с новыми агентами, используйте код ниже:
# # он загружает агента из файла
# agent = SimpleCarAgent.from_file('filename.txt')
# # создаёт мир
# w = SimpleCarWorld(1, m, SimplePhysics, SimpleCarAgent, timedelta=0.2)
# # подключает к нему агента
# w.set_agents([agent])
# # и запускается
# w.run()
# # или оценивает агента в этом мире
# print(w.evaluate_agent(agent, 500))
def run_and_save_best(visual, steps, _map=None, file=None):
"""
Trains multiple networks with different hyperparameters, chooses the network
with the best result and saves in to a file.
:param file: File
"""
# create worlds to run the agents on
if _map is None:
worlds_number = 3
np.random.seed(None)
random.seed(None)
_map = generate_map()
worlds = list(SimpleCarWorld(1, _map, SimplePhysics, SimpleCarAgent, visual, timedelta=0.2) for _ in range(worlds_number))
else:
worlds = [SimpleCarWorld(1, _map, SimplePhysics, SimpleCarAgent, visual, timedelta=0.2)]
# create agents with all possible hyperparameters
agents = []
for (eta, reg_coef, epochs, reward_depth, train_every) \
in list(itertools.product(
# etas
[1e-01],
# reg_coefs
[32],
# epochs
[60],
# reward_depth
[15],
# train_every / batch size
[50]
)):
if file is None:
print("Creating a new agent")
agent = SimpleCarAgent()
else:
print(f"Using an agent with weights from {file}")
agent = SimpleCarAgent.from_file(file)
if eta is not None:
agent.eta = eta
if reg_coef is not None:
agent.reg_coef = reg_coef
if epochs is not None:
agent.epochs = epochs
if reward_depth is not None:
agent.reward_depth = reward_depth
if train_every is not None:
agent.train_every = train_every
agents += [agent]
errors = []
for world in worlds:
errors += [run_agent_for_worlds(agents, world, steps)]
means = np.nanmean(errors, 0)[0]
results = dict(zip(agents, means))
best_agent = max(results, key=results.get)
best_reward = results[best_agent]
if type(best_reward) is not np.float64:
best_reward = None
for agent, result in results.items():
print(f"Creating an agent with hyperparams: \n{agent.hyperparams_to_string()} \nError: {result}\n")
print(f"🏆 This agent performed the best in all worlds with the error {best_reward}\n{best_agent.hyperparams_to_string()}")
# write results to files
file_path = str(file)
dot_index = file_path.find(".")
reward_file = file[:dot_index] + latest_error_file_suffix + file[dot_index:]
with open(reward_file, 'a+') as f:
f.seek(0)
lines = f.readlines()
last_reward = lines[-1] if len(lines) > 0 else None
if best_reward is not None and (file is None or last_reward is None or float(last_reward) < float(best_reward)):
save_to_file(best_agent)
if last_reward is not None:
f.write('\n')
f.write(str(best_reward))
else:
save_to_file(agent=best_agent, prefix="temp_")
print(f"Reward ({best_reward}) was invalid or worse than {last_reward} and was saved to a temporary file")
parser = argparse.ArgumentParser()
parser.add_argument("-s", "--steps", type=int)
parser.add_argument("-f", "--filename", type=str)
parser.add_argument("-e", "--evaluate", type=bool)
parser.add_argument("--seed", type=int)
args = parser.parse_args()
args.steps = 1000
args.seed = 100
args.filename = 'network_config_agent_0_layers_11_6_1.txt'
print(args.steps, args.seed, args.filename, args.evaluate)
steps = args.steps
seed = args.seed if args.seed else 23
np.random.seed(seed)
random.seed(seed)
m = generate_map(8, 5, 3, 3)
if args.filename:
agent = SimpleCarAgent.from_file(args.filename)
w = SimpleCarWorld(5, m, SimplePhysics, SimpleCarAgent, timedelta=0.2)
if args.evaluate:
print(w.evaluate_agent(agent, steps))
else:
w.set_agents([agent])
w.run(steps)
else:
SimpleCarWorld(5, m, SimplePhysics, SimpleCarAgent,
timedelta=0.2).run(steps)
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("-s", "--steps", type=int)
parser.add_argument("-f", "--filename", type=str)
parser.add_argument("-e", "--evaluate", type=bool)
parser.add_argument("--seed", type=int)
args = parser.parse_args()
print(args.steps, args.seed, args.filename, args.evaluate)
steps = args.steps
seed = args.seed if args.seed else 23
np.random.seed(seed)
random.seed(seed)
m = generate_map(20, 2, 1, 1)
radii = np.random.normal(loc=2.5, scale=0.2, size=8)
angles = get_partition(8, -np.pi, np.pi)
o = generate_obstacles(8, radii, angles, 0)
if args.filename:
agent = SimpleCarAgent.from_file(args.filename)
w = SimpleCarWorld(1, m, o, radii, angles, SimplePhysics, SimpleCarAgent, timedelta=0.2)
if args.evaluate:
with open('results.txt', 'a') as inf:
circles, collisions = w.evaluate_agent(agent, SimplePhysics, steps, timedelta=0.2)
inf.write('seed: {} circles: {} collisions: {} \n'.format(seed, circles, collisions))
else:
w.set_agents([agent])
w.run(SimplePhysics, steps, timedelta=0.2)
else: