def main():
# Load the agent from the submission
print('Loading your agent')
try:
spec = importlib.util.spec_from_file_location('agent_module',
'/aaio/agent.py')
agent_module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(agent_module)
submitted_agent = agent_module.Agent()
except Exception as e:
print(
'Your agent could not be loaded, make sure all the paths are absolute, error thrown:'
)
raise e
print('Agent successfully loaded')
arena_config_in = ArenaConfig('/aaio/test/1-Food.yaml')
print('Resetting your agent')
try:
submitted_agent.reset(t=arena_config_in.arenas[0].t)
except Exception as e:
print('Your agent could not be reset:')
raise e
env = AnimalAIEnv(
environment_filename='/aaio/test/env/AnimalAI',
# seed=0,
retro=False,
n_arenas=1,
worker_id=1,
docker_training=True,
)
env.reset(arenas_configurations=arena_config_in)
obs, reward, done, info = env.step([0, 0])
print('Running 5 episodes')
for k in range(5):
cumulated_reward = 0
print('Episode {} starting'.format(k))
try:
for i in range(arena_config_in.arenas[0].t):
action = submitted_agent.step(obs, reward, done, info)
obs, reward, done, info = env.step(action)
cumulated_reward += reward
if done:
break
except Exception as e:
print('Episode {} failed'.format(k))
raise e
print('Episode {0} completed, reward {1}'.format(k, cumulated_reward))
print('SUCCESS')
import cv2
env_path = '../env/AnimalAI'
worker_id = random.randint(1, 100)
arena_config_in = ArenaConfig('ordered_configs/avoid_red/1-25-1.yml')
env = AnimalAIEnv(environment_filename=env_path,
worker_id=worker_id,
n_arenas=1,
arenas_configurations=arena_config_in,
docker_training=False,
retro=False)
done = False
number_of_episodes = 10
i = 0
SKIPPED_FRAMES = 10
state = env.reset()
print(state[0].shape)
print(state[0].ndim)
print(env.action_space.sample())
numpy_image = state[0] * 255
numpy_image = numpy_image.astype('uint8')
cv_image = cv2.cvtColor(numpy_image, cv2.COLOR_BGR2RGB)
resized = cv2.resize(cv_image, (300, 300), interpolation = cv2.INTER_AREA)
cv2.imwrite('kk.png', resized)
state, reward, done, info = env.step(env.action_space.sample())
env.close()
worker_id = 100
env = AnimalAIEnv(environment_filename=env_path,
worker_id=worker_id,
n_arenas=1,
arenas_configurations=None,
docker_training=False,
retro=False)
for dir_name in os.listdir(path):
if not os.path.exists('./datasets/{}'.format(dir_name)):
os.makedirs('./datasets/{}'.format(dir_name))
path_to_dir = os.path.join(path, dir_name)
for file in os.listdir(path_to_dir):
arena_file_path = os.path.join(path_to_dir, file)
arena_config_in = ArenaConfig(arena_file_path)
env.reset(arena_config_in)
print(arena_file_path)
i = 0
done = False
try:
while True and i < number_of_images:
done = False
state = env.reset(arena_config_in)
while not done and i < number_of_images:
numpy_image = state[0] * 255
numpy_image = numpy_image.astype('uint8')
cv_image = cv2.cvtColor(numpy_image, cv2.COLOR_BGR2RGB)
resized = cv2.resize(cv_image, (300, 300),
interpolation=cv2.INTER_AREA)
cv2.imwrite(
'./datasets/{}/{}_{}.png'.format(