def create_env_fn():
env = AnimalAIEnv(environment_filename=env_path,
worker_id=worker_id,
n_arenas=1,
arenas_configurations=arena_config_in,
retro=True)
return env
def create_animal(num_actors=1, inference=True, config=None, seed=None):
from animalai.envs.gym.environment import AnimalAIEnv
from animalai.envs.arena_config import ArenaConfig
import random
from animalai_wrapper import AnimalWrapper, AnimalStack, AnimalSkip
env_path = 'AnimalAI'
worker_id = random.randint(1, 60000)
arena_config_in = ArenaConfig(
BASE_DIR + '/configs/learning/stage4/3-Food Moving.yaml')
if config is None:
config = arena_config_in
else:
config = ArenaConfig(config)
if seed is None:
seed = 0 #random.randint(0, 100500)
env = AnimalAIEnv(environment_filename=env_path,
worker_id=worker_id,
n_arenas=num_actors,
seed=seed,
arenas_configurations=config,
greyscale=False,
docker_training=False,
inference=inference,
retro=False,
resolution=84)
env = AnimalSkip(env, skip=SKIP_FRAMES)
env = AnimalWrapper(env)
env = AnimalStack(env,
VISUAL_FRAMES_COUNT,
VEL_FRAMES_COUNT,
greyscale=USE_GREYSCALE_OBSES)
return env
def create_env_fn():
env = AnimalAIEnv(environment_filename=ENV_PATH,
worker_id=WORKER_ID,
n_arenas=1,
arenas_configurations=ArenaConfig(args.arena_config),
docker_training=False,
retro=False)
return env
def create_env_fn():
env = AnimalAIEnv(environment_filename=env_path,
worker_id=worker_id,
n_arenas=1,
arenas_configurations=arena_config_in,
docker_training=False,
retro=True,
inference=watch)
return env
def init_env(env_path, env_seed):
env = AnimalAIEnv(environment_filename=env_path,
seed=env_seed,
retro=False,
n_arenas=1,
worker_id=1,
docker_training=False,
resolution=84)
return env
def create_env_fn():
env = AnimalAIEnv(environment_filename=env_path,
worker_id=worker_id,
n_arenas=1,
arenas_configurations=arena_config_in,
docker_training=False,
retro=True)
print("all good create_env_fn")
return env
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')
def create_animalai_environment(environment_path=None):
"""Wraps the Animal AI environment with some basic preprocessing.
Returns:
An Animal AI environment with some standard preprocessing.
"""
assert environment_path is not None
env = AnimalAIEnv(environment_path, 0, n_arenas=1, retro=True)
env = OTCPreprocessing(env)
return env
def create_env_fn(self):
env = AnimalAIEnv(environment_filename=self.env_path,
worker_id=self.worker_id,
n_arenas=1,
arenas_configurations=self.arena_config_in,
docker_training=False,
retro=False,
inference=self.args['inference'],
seed=self.rank,
resolution=84)
return env
def create_env_fn():
env = AnimalAIEnv(environment_filename=env_path,
worker_id=worker_id,
n_arenas=1,
arenas_configurations=arena_config_in,
docker_training=False,
retro=False,
inference=True,
resolution=84,
seed=random.randint(0, 100))
return env
def env():
worker_id = random.randint(1, 60000)
env = AnimalAIEnv(environment_filename=env_path,
worker_id=worker_id,
n_arenas=num_actors,
seed = seed,
arenas_configurations=config,
greyscale = False,
docker_training=False,
inference = inference,
retro=False,
resolution=84
)
env = AnimalSkip(env, skip=SKIP_FRAMES)
env = AnimalWrapper(env)
env = AnimalStack(env,VISUAL_FRAMES_COUNT, VEL_FRAMES_COUNT, greyscale=USE_GREYSCALE_OBSES)
return env
from animalai.envs.gym.environment import AnimalAIEnv
from animalai.envs.arena_config import ArenaConfig
import numpy as np
import random
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)
def process_info(self, info):
return {}
parser = argparse.ArgumentParser()
parser.add_argument('--mode', choices=['train', 'test'], default='train')
parser.add_argument('--weights', type=str, default=None)
parser.add_argument('--config-file', type=str, default=None)
args = parser.parse_args()
arena_config_in = ArenaConfig(args.config_file)
# Get the environment and extract the number of ENV_NAME
ENV_NAME = "AnimalAIEnv"
env = AnimalAIEnv(environment_filename='../env90deg/AnimalAILinux',
worker_id=worker_id,
n_arenas=1,
arenas_configurations=arena_config_in,
docker_training=False,
retro=False)
np.random.seed(123)
env.seed(123)
nb_actions = 5
# Next, we build our model. We use the same model that was described by Mnih et al. (2015).
input_shape = (WINDOW_LENGTH,) + INPUT_SHAPE
print(input_shape)
model = Sequential()
# if K.common.image_dim_ordering() == 'tf':
# # (width, height, channels)
model.add(Permute((2, 3, 1), input_shape=input_shape))
model.add(Convolution2D(32, (8, 8), strides=(4, 4)))