def __init__(self, trainer: Trainer.__class__, weights: str):
if not ray.is_initialized():
ray.init()
self.obs_state_processor = SimpleObsStateProcessor(
infected_population_sorting_per_city)
self.act_state_processor = SimpleActStateProcessor(
sort_pathogens=self.obs_state_processor.sort_pathogens)
register_env(
"ic20env", lambda _: InferenceIC20Environment(
self.obs_state_processor, self.act_state_processor))
self.trainer = self._load_trainer(trainer(env="ic20env"), weights)
from ray.tune.logger import pretty_print
from ray.tune.util import merge_dicts
from approaches.reinforced.action_state_processor import SimpleActStateProcessor
from approaches.reinforced.constants import DEFAULT_CONFIG
from approaches.reinforced.environment import SimplifiedIC20Environment, CHECKPOINT_FILE
from approaches.reinforced.observation_state_processor import SimpleObsStateProcessor, \
infected_population_sorting_per_city
# won't start sgd
from approaches.reinforced.reward_function import UnstableReward
if __name__ == "__main__":
ray.init(
address='auto'
) # address = None when running locally. address = 'auto' when running on aws.]
obs_state_processor = SimpleObsStateProcessor(
pathogen_sorting_strategy=infected_population_sorting_per_city)
act_state_processor = SimpleActStateProcessor(
sort_pathogens=obs_state_processor.sort_pathogens)
# Notice that trial_max will only work for stochastic policies
register_env(
"ic20env", lambda _: SimplifiedIC20Environment(obs_state_processor,
act_state_processor,
UnstableReward(),
trial_max=10))
ten_gig = 10737418240
trainer = A2CTrainer(
env="ic20env",
config=merge_dicts(
DEFAULT_CONFIG,
def simple_obs_state_processor(
available_pathogens: List[Pathogen]) -> SimpleObsStateProcessor:
def identity_ordering(city: City, pathogen: Pathogen) -> float:
return available_pathogens.index(pathogen)
return SimpleObsStateProcessor(identity_ordering)