def __init__(self,
minio_bucket,
manager_host,
manager_port,
lock_server_host,
lock_server_port,
worker_id,
policy_class_name,
policy_config_key,
provide_payoff_barrier_sync=False):
worker_id = f"live_pop_tracker_{worker_id[worker_id.find('pid'):]}"
self._storage_client = connect_storage_client()
self._minio_bucket = minio_bucket
self._manager_interface = ConsoleManagerInterface(
server_host=manager_host,
port=manager_port,
worker_id=worker_id,
storage_client=self._storage_client,
minio_bucket_name=self._minio_bucket)
self._lock_interface = LockServerInterface(
server_host=lock_server_host,
port=lock_server_port,
worker_id=worker_id)
self._policy_class_name = policy_class_name
self._policy_config_key = policy_config_key
self._claimed_policy_num = None
self._claim_new_active_policy()
assert self._claimed_policy_num is not None
self._locally_cached_matchup_results = {}
self._provide_payoff_barrier_sync = provide_payoff_barrier_sync
if self._provide_payoff_barrier_sync:
self._wait_at_payoff_table_barrier_fn, self._leave_barrier_group_fn = self._lock_interface.join_barrier_group(
barrier_name="pt_barrier",
member_name=str(self._claimed_policy_num),
grace_period_for_others_to_join_s=20.0)
else:
self._wait_at_payoff_table_barrier_fn = None
self._leave_barrier_group_fn = None
CLOUD_PREFIX = os.getenv("CLOUD_PREFIX", "")
if __name__ == "__main__":
expected_payoff_matrix_size = 0
while True:
logging.basicConfig(level=logging.DEBUG)
logger.info("\n\n\n\n\n__________________________________________\n"
f"LAUNCHED FOR {POKER_GAME_VERSION}\n"
f"__________________________________________\n\n\n\n\n")
storage_client = connect_storage_client()
size_checker = ConsoleManagerInterface(
server_host=MANAGER_SEVER_HOST,
port=MANAGER_PORT,
worker_id=f"size_checker_{gethostname()}_pid_{os.getpid()}",
storage_client=storage_client,
minio_bucket_name=BUCKET_NAME)
while True:
current_payoff_matrix_size = size_checker.get_size_of_current_payoff_table(
)
if current_payoff_matrix_size < expected_payoff_matrix_size:
logger.info(
f"waiting for payoff matrix to reach size {expected_payoff_matrix_size} (currently {current_payoff_matrix_size})..."
)
time.sleep(5)
# elif current_payoff_matrix_size > expected_payoff_matrix_size:
# raise ValueError(f"payoff matrix is now larger than expected (expected {expected_payoff_matrix_size}, currently {current_payoff_matrix_size})")
else:
break
CLOUD_PREFIX = os.getenv("CLOUD_PREFIX", "")
if __name__ == "__main__":
new_learner_wait_for_key_in_payoff_table = None
while True:
logging.basicConfig(level=logging.DEBUG)
logger.info("\n\n\n\n\n__________________________________________\n"
f"LAUNCHED FOR {POKER_GAME_VERSION}\n"
f"__________________________________________\n\n\n\n\n")
storage_client = connect_storage_client()
size_checker = ConsoleManagerInterface(
server_host=MANAGER_SEVER_HOST,
port=MANAGER_PORT,
worker_id=f"size_checker_{gethostname()}_pid_{os.getpid()}",
storage_client=storage_client,
minio_bucket_name=BUCKET_NAME)
while True:
if new_learner_wait_for_key_in_payoff_table is not None:
if not size_checker.is_policy_key_in_current_payoff_matrix(
policy_key=new_learner_wait_for_key_in_payoff_table):
logger.info(
f"waiting for payoff matrix to include the policy key {new_learner_wait_for_key_in_payoff_table} "
)
time.sleep(5)
else:
break
else:
break
class LivePolicyPayoffTracker(object):
def __init__(self,
minio_bucket,
manager_host,
manager_port,
lock_server_host,
lock_server_port,
worker_id,
policy_class_name,
policy_config_key,
provide_payoff_barrier_sync=False):
worker_id = f"live_pop_tracker_{worker_id[worker_id.find('pid'):]}"
self._storage_client = connect_storage_client()
self._minio_bucket = minio_bucket
self._manager_interface = ConsoleManagerInterface(
server_host=manager_host,
port=manager_port,
worker_id=worker_id,
storage_client=self._storage_client,
minio_bucket_name=self._minio_bucket)
self._lock_interface = LockServerInterface(
server_host=lock_server_host,
port=lock_server_port,
worker_id=worker_id)
self._policy_class_name = policy_class_name
self._policy_config_key = policy_config_key
self._claimed_policy_num = None
self._claim_new_active_policy()
assert self._claimed_policy_num is not None
self._locally_cached_matchup_results = {}
self._provide_payoff_barrier_sync = provide_payoff_barrier_sync
if self._provide_payoff_barrier_sync:
self._wait_at_payoff_table_barrier_fn, self._leave_barrier_group_fn = self._lock_interface.join_barrier_group(
barrier_name="pt_barrier",
member_name=str(self._claimed_policy_num),
grace_period_for_others_to_join_s=20.0)
else:
self._wait_at_payoff_table_barrier_fn = None
self._leave_barrier_group_fn = None
@ray.method(num_return_vals=1)
def wait_at_barrier_for_other_learners(self):
assert self._provide_payoff_barrier_sync
self._wait_at_payoff_table_barrier_fn()
return True
@ray.method(num_return_vals=1)
def set_latest_key_for_claimed_policy(
self, new_key, request_locks_checkpoint_with_name=None):
assert self._claimed_policy_num is not None
prefix = f"policy {self._claimed_policy_num} latest key: "
new_lock = prefix + new_key
policy_key_locks = self._lock_interface.get_all_items(
filter_by_string=prefix)
if len(policy_key_locks) > 0:
assert len(policy_key_locks) == 1
old_lock = policy_key_locks[0]
assert self._lock_interface.replace_item(
old_item=old_lock,
new_item=new_lock,
new_item_remains_after_disconnect=True,
request_locks_checkpoint_with_name=
request_locks_checkpoint_with_name)
print(
colored(
f"Policy {self._claimed_policy_num}: Set new latest key for claimed policy (replaced old one): \"{new_lock}\"",
'green'))
else:
assert self._lock_interface.try_to_reserve_item(
item_name=new_lock,
remain_after_worker_disconnect=True,
request_locks_checkpoint_with_name=
request_locks_checkpoint_with_name)
print(
colored(
f"Policy {self._claimed_policy_num}: Set new latest key for claimed policy: \"{new_lock}\"",
"green"))
return True
@ray.method(num_return_vals=1)
def set_claimed_policy_as_finished(self):
old_lock = f"policy_status: {self._claimed_policy_num} active"
new_lock = f"policy_status: {self._claimed_policy_num} finished"
assert self._lock_interface.replace_item(
old_item=old_lock,
new_item=new_lock,
new_item_remains_after_disconnect=True)
print(
colored(
f"Policy {self._claimed_policy_num}: Set claimed policy as finished: \"{new_lock}\"",
"green"))
if self._leave_barrier_group_fn is not None:
self._leave_barrier_group_fn()
return True
@ray.method(num_return_vals=2)
def get_live_payoff_table_dill_pickled(self,
first_wait_for_n_seconds=None):
if first_wait_for_n_seconds is not None:
time.sleep(first_wait_for_n_seconds)
base_payoff_table, _ = self._manager_interface.get_latest_payoff_table(
infinite_retry_on_error=False)
if base_payoff_table is None:
base_payoff_table = PayoffTable()
base_payoff_table: PayoffTable = base_payoff_table
active_policy_numbers, finished_policy_numbers, total_policy_numbers = self._get_active_and_finished_policy_numbers(
)
assert len(active_policy_numbers) + len(
finished_policy_numbers) == total_policy_numbers
are_all_lower_policies_finished = len(active_policy_numbers) == 0
print(
colored(
f"Policy {self._claimed_policy_num}: Latest live stats for policies below this learner: {len(finished_policy_numbers)} policies finished, {len(active_policy_numbers)} active.",
"cyan"))
if total_policy_numbers == 0:
return None, are_all_lower_policies_finished
assert base_payoff_table.size() <= len(
finished_policy_numbers) or base_payoff_table.size() == 1
missing_policy_nums = list(
range(base_payoff_table.size(), total_policy_numbers))
for missing_policy_num in missing_policy_nums:
missing_key = self._get_latest_key_for_policy_number(
policy_num=missing_policy_num)
if missing_key is None:
time.sleep(5)
missing_key = self._get_latest_key_for_policy_number(
policy_num=missing_policy_num)
if missing_key is not None:
base_payoff_table.add_policy(
new_policy_key=missing_key,
new_policy_class_name=self._policy_class_name,
new_policy_config_file_key=self._policy_config_key,
new_policy_tags=['locally_tracked'])
required_evals_observed = set()
required_evals_finalized = set()
while True:
matchup_order = base_payoff_table.get_eval_matchup_order()
if matchup_order is None:
break
if matchup_order not in required_evals_finalized:
as_policy_key, against_policy_key = matchup_order
payoff, games_played = self._check_eval_cache(
as_policy_key=as_policy_key,
against_policy_key=against_policy_key)
if payoff is None:
payoff, games_played = self._manager_interface.request_eval_result(
as_policy_key=as_policy_key,
as_policy_config_key=self._policy_config_key,
as_policy_class_name=self._policy_class_name,
against_policy_key=against_policy_key,
against_policy_config_key=self._policy_config_key,
against_policy_class_name=self._policy_class_name,
perform_eval_if_not_cached=matchup_order
not in required_evals_observed,
infinite_retry_on_error=False)
if payoff is not None and matchup_order not in required_evals_observed:
print(
f"{colored(f'Policy {self._claimed_policy_num}: !!!! GOT A CACHE HIT FROM THE MANAGER !!!!','yellow')}\n"
f"{colored(f'for {as_policy_key} vs {against_policy_key}', 'yellow')}"
)
if payoff is None and matchup_order in required_evals_observed:
print(
colored(
f"Policy {self._claimed_policy_num}: Waiting to get eval result for {as_policy_key} vs {against_policy_key}",
"yellow"))
time.sleep(2)
if payoff is not None:
self._add_to_eval_cache_if_not_already_entered(
as_policy_key=as_policy_key,
against_policy_key=against_policy_key,
payoff=payoff,
games_played=games_played)
base_payoff_table.add_eval_result(
as_policy_key=as_policy_key,
against_policy_key=against_policy_key,
payoff=payoff,
games_played=games_played)
required_evals_finalized.add(matchup_order)
required_evals_observed.add(matchup_order)
assert len(required_evals_observed) >= len(required_evals_finalized)
assert base_payoff_table.get_num_pending_policies(
) == 0, f"amount is {base_payoff_table.get_num_pending_policies()}"
assert base_payoff_table.size() == total_policy_numbers
return base_payoff_table.to_dill(), are_all_lower_policies_finished
####################################################################################################################
@ray.method(num_return_vals=1)
def my_get_live_payoff_table_dill_pickled(self,
new_weight_key=None,
first_wait_for_n_seconds=None):
if first_wait_for_n_seconds is not None:
time.sleep(first_wait_for_n_seconds)
base_payoff_table, _ = self._manager_interface.get_latest_payoff_table(
infinite_retry_on_error=False)
if base_payoff_table is None:
base_payoff_table = PayoffTable()
base_payoff_table: PayoffTable = base_payoff_table
active_policy_numbers, finished_policy_numbers, total_policy_numbers = self._get_active_and_finished_policy_numbers(
)
assert len(active_policy_numbers) + len(
finished_policy_numbers) + 1 == total_policy_numbers + 1
are_all_lower_policies_finished = len(active_policy_numbers) == 0
# print(colored(f"Policy {self._claimed_policy_num}: Latest live stats for policies below this learner: "
# f"{len(finished_policy_numbers)} policies finished, {len(active_policy_numbers)} active.", "magenta"))
if total_policy_numbers + 1 == 0:
return None
# Payoff tables smaller than 2x2 are not interesting for diversity computation
if base_payoff_table.size() < 3:
return None
assert base_payoff_table.size() <= len(
finished_policy_numbers) or base_payoff_table.size() == 1
missing_policy_nums = list(
range(base_payoff_table.size(), total_policy_numbers + 1))
for missing_policy_num in missing_policy_nums:
if missing_policy_num < missing_policy_nums[-1]:
missing_key = self._get_latest_key_for_policy_number(
policy_num=missing_policy_num)
else:
missing_key = new_weight_key
# print(colored(f" My key: {missing_key} ", "magenta"))
if missing_key is None:
time.sleep(5)
missing_key = self._get_latest_key_for_policy_number(
policy_num=missing_policy_num)
if missing_key is not None:
base_payoff_table.add_policy(
new_policy_key=missing_key,
new_policy_class_name=self._policy_class_name,
new_policy_config_file_key=self._policy_config_key,
new_policy_tags=['locally_tracked'])
required_evals_observed = set()
required_evals_finalized = set()
while True:
matchup_order = base_payoff_table.get_eval_matchup_order()
if matchup_order is None:
break
if matchup_order not in required_evals_finalized:
as_policy_key, against_policy_key = matchup_order
payoff, games_played = self._check_eval_cache(
as_policy_key=as_policy_key,
against_policy_key=against_policy_key)
if payoff is None:
payoff, games_played = self._manager_interface.request_eval_result(
as_policy_key=as_policy_key,
as_policy_config_key=self._policy_config_key,
as_policy_class_name=self._policy_class_name,
against_policy_key=against_policy_key,
against_policy_config_key=self._policy_config_key,
against_policy_class_name=self._policy_class_name,
perform_eval_if_not_cached=matchup_order
not in required_evals_observed,
infinite_retry_on_error=False)
if payoff is not None and matchup_order not in required_evals_observed:
print(
f"{colored(f'Policy {self._claimed_policy_num}: !!!! GOT A CACHE HIT FROM THE MANAGER !!!!','yellow')}\n"
f"{colored(f'for {as_policy_key} vs {against_policy_key}', 'yellow')}"
)
if payoff is None and matchup_order in required_evals_observed:
print(
colored(
f"Policy {self._claimed_policy_num}: Waiting to get eval result for {as_policy_key} vs {against_policy_key}",
"yellow"))
time.sleep(2)
if payoff is not None:
self._add_to_eval_cache_if_not_already_entered(
as_policy_key=as_policy_key,
against_policy_key=against_policy_key,
payoff=payoff,
games_played=games_played)
base_payoff_table.add_eval_result(
as_policy_key=as_policy_key,
against_policy_key=against_policy_key,
payoff=payoff,
games_played=games_played)
required_evals_finalized.add(matchup_order)
required_evals_observed.add(matchup_order)
assert len(required_evals_observed) >= len(required_evals_finalized)
assert base_payoff_table.get_num_pending_policies(
) == 0, f"amount is {base_payoff_table.get_num_pending_policies()}"
assert base_payoff_table.size(
) == total_policy_numbers + 1, f"base_payoff_table.size() is {base_payoff_table.size()}, total_policy_numbers+1={total_policy_numbers+1}"
return base_payoff_table.get_payoff_matrix()
####################################################################################################################
@ray.method(num_return_vals=1)
def are_all_lower_policies_finished(self):
active_policy_numbers, finished_policy_numbers, total_policy_numbers = self._get_active_and_finished_policy_numbers(
)
assert len(active_policy_numbers) + len(
finished_policy_numbers) == total_policy_numbers
return len(active_policy_numbers) == 0
@ray.method(num_return_vals=1)
def get_claimed_policy_num(self):
return self._claimed_policy_num
def _get_active_and_finished_policy_numbers(self):
start_time = time.time()
while True:
policy_status_locks = self._lock_interface.get_all_items(
filter_by_string="policy_status: ")
if len(policy_status_locks) == 0:
return [], [], 0
_, all_policy_numbers, all_policy_statuses = map(
list, zip(*[item.split(" ") for item in policy_status_locks]))
assert all(stat == "active" or stat == "finished"
for stat in all_policy_statuses)
num_policies_to_consider = self._claimed_policy_num if self._claimed_policy_num is not None else len(
all_policy_numbers)
policy_numbers = [None] * num_policies_to_consider
policies_active_states = [None] * num_policies_to_consider
for policy_num, policy_status in zip(all_policy_numbers,
all_policy_statuses):
policy_num = int(policy_num)
if self._claimed_policy_num is None or policy_num < self._claimed_policy_num:
policy_numbers[policy_num] = policy_num
policies_active_states[policy_num] = (
policy_status == "active")
if not all(p is not None for p in policy_numbers):
if time.time() - start_time > 60:
raise ValueError(
colored(
f"policy_numbers (some are None): {policy_numbers}",
"red"))
print(
colored(
f"policy_numbers (some are None), trying again: {policy_numbers}",
"red"))
time.sleep(0.5)
continue
assert all(p is not None for p in policies_active_states)
assert _check_consecutive_numbers(
int_list=policy_numbers, should_start_at=0
), f"policy_numbers is {policy_numbers}, all policy status locks are {policy_status_locks}"
assert _check_only_latest_policies_are_active(
policies_active_states=policies_active_states)
break
active_policy_numbers = []
finished_policy_numbers = []
for i, policy_number in enumerate(policy_numbers):
if policies_active_states[i]:
active_policy_numbers.append(policy_number)
else:
finished_policy_numbers.append(policy_number)
total_policy_numbers = len(policy_numbers)
return active_policy_numbers, finished_policy_numbers, total_policy_numbers
def _claim_new_active_policy(self):
if self._claimed_policy_num is not None:
raise ValueError(
f"This interface has already claimed policy {self._claimed_policy_num}"
)
_, _, total_policy_numbers = self._get_active_and_finished_policy_numbers(
)
claimed_policy_key = self._lock_interface.try_to_reserve_item_from_list(
possible_item_names_in_order_of_highest_priority_first=[
f"policy_status: {i} active"
for i in range(total_policy_numbers, total_policy_numbers +
100)
])
claimed_policy_num = int(
claimed_policy_key.replace('policy_status: ',
'').replace(' active', ''))
assert claimed_policy_num is not None
print(colored(f"Claimed Policy {claimed_policy_num}", "green"))
self._claimed_policy_num = claimed_policy_num
return claimed_policy_num
def _get_latest_key_for_policy_number(self, policy_num):
prefix = f"policy {policy_num} latest key: "
policy_key_locks = self._lock_interface.get_all_items(
filter_by_string=prefix)
if len(policy_key_locks) == 0:
return None
assert len(policy_key_locks) == 1
policy_key = policy_key_locks[0][len(prefix):]
return policy_key
def _check_eval_cache(self, as_policy_key, against_policy_key):
payoff, games_played = None, None
try:
payoff, games_played = self._locally_cached_matchup_results[
as_policy_key][against_policy_key]
print(
colored(
f"Eval Cache Hit for \"{as_policy_key}\" vs \"{against_policy_key}\"",
"green"))
except KeyError:
try:
payoff, games_played = self._locally_cached_matchup_results[
against_policy_key][as_policy_key]
payoff = -payoff
print(
colored(
f"Eval Cache Hit for \"{against_policy_key}\" vs \"{as_policy_key}\"",
"green"))
except KeyError:
pass
return payoff, games_played
def _add_to_eval_cache_if_not_already_entered(self, as_policy_key,
against_policy_key, payoff,
games_played):
old_payoff, _ = self._check_eval_cache(
as_policy_key=as_policy_key, against_policy_key=against_policy_key)
if old_payoff is not None:
return
if as_policy_key not in self._locally_cached_matchup_results:
self._locally_cached_matchup_results[as_policy_key] = {}
if against_policy_key not in self._locally_cached_matchup_results[
as_policy_key]:
self._locally_cached_matchup_results[as_policy_key][
against_policy_key] = (payoff, games_played)
def measure_exploitability_of_metanashes_as_they_become_available():
logger = get_logger()
storage_client = connect_storage_client()
worker_id = f"Exploitability_Tracker_{gethostname()}_pid_{os.getpid()}_{datetime_str()}"
manager_interface = ConsoleManagerInterface(
server_host=MANAGER_SEVER_HOST,
port=MANAGER_PORT,
worker_id=worker_id,
storage_client=storage_client,
minio_bucket_name=BUCKET_NAME,
minio_local_dir=DEFAULT_LOCAL_SAVE_PATH)
logger.info(f"Started worker \'{worker_id}\'")
# If you use ray for more than just this single example fn, you'll need to move ray.init to the top of your main()
ray.init(address=os.getenv('RAY_HEAD_NODE'),
ignore_reinit_error=True,
local_mode=True)
model_config_file_path, _ = maybe_download_object(
storage_client=storage_client,
bucket_name=BUCKET_NAME,
object_name=MODEL_CONFIG_KEY,
force_download=False)
with open(model_config_file_path, 'r') as config_file:
model_config = json.load(fp=config_file)
example_env = PokerMultiAgentEnv(env_config=POKER_ENV_CONFIG)
logger.info("\n\n\n\n\n__________________________________________\n"
f"LAUNCHED FOR {POKER_GAME_VERSION}\n"
f"__________________________________________\n\n\n\n\n")
obs_space = example_env.observation_space
act_space = example_env.action_space
preprocessor = StrategoDictFlatteningPreprocessor(obs_space=obs_space)
graph = tf.Graph()
sess = tf.Session(config=tf.ConfigProto(device_count={'GPU': 0}),
graph=graph)
def fetch_logits(policy):
return {
"behaviour_logits": policy.model.last_output(),
}
_policy_cls = POLICY_CLASS.with_updates(
extra_action_fetches_fn=fetch_logits)
with graph.as_default():
with sess.as_default():
policy = _policy_cls(obs_space=preprocessor.observation_space,
action_space=act_space,
config=with_common_config({
'model':
with_base_config(
base_config=MODEL_DEFAULTS,
extra_config=model_config),
'env':
POKER_ENV,
'env_config':
POKER_ENV_CONFIG,
'custom_preprocessor':
STRATEGO_PREPROCESSOR
}))
def set_policy_weights(weights_key):
weights_file_path, _ = maybe_download_object(
storage_client=storage_client,
bucket_name=BUCKET_NAME,
object_name=weights_key,
force_download=False)
policy.load_model_weights(weights_file_path)
print("(Started Successfully)")
last_payoff_table_key = None
while True:
payoff_table, payoff_table_key = manager_interface.get_latest_payoff_table(
infinite_retry_on_error=True)
if payoff_table_key == last_payoff_table_key:
time.sleep(20)
continue
last_payoff_table_key = payoff_table_key
metanash_probs, _, _ = get_fp_metanash_for_latest_payoff_table(
manager_interface=manager_interface,
fp_iters=20000,
accepted_opponent_policy_class_names=[POLICY_CLASS_NAME],
accepted_opponent_model_config_keys=[POKER_ENV_CONFIG],
add_payoff_matrix_noise_std_dev=0.000,
mix_with_uniform_dist_coeff=None,
p_or_lower_rounds_to_zero=0.0)
if metanash_probs is not None:
policy_weights_keys = payoff_table.get_ordered_keys_in_payoff_matrix(
)
policy_dict = {
key: prob
for key, prob in zip(policy_weights_keys, metanash_probs)
}
exploitabilitly = measure_exploitability_nonlstm(
rllib_policy=policy,
poker_game_version=POKER_GAME_VERSION,
policy_mixture_dict=policy_dict,
set_policy_weights_fn=set_policy_weights)
print(f"Exploitability: {exploitabilitly}")
CANT_SUBMIT_UNTIL_LOWER_POLICIES_FINISH = True
INIT_FROM_POPULATION = POKER_PIPELINE_INIT_FROM_POP
CLOUD_PREFIX = os.getenv("CLOUD_PREFIX", "")
if __name__ == "__main__":
while True:
logging.basicConfig(level=logging.DEBUG)
logger.info("\n\n\n\n\n__________________________________________\n"
f"LAUNCHED FOR {POKER_GAME_VERSION}\n"
f"__________________________________________\n\n\n\n\n")
storage_client = connect_storage_client()
size_checker = ConsoleManagerInterface(
server_host=MANAGER_SERVER_HOST,
port=MANAGER_PORT,
worker_id=f"size_checker_{gethostname()}_pid_{os.getpid()}",
storage_client=storage_client,
minio_bucket_name=BUCKET_NAME)
ray.init(address=os.getenv('RAY_HEAD_NODE'),
ignore_reinit_error=True,
log_to_driver=True)
logger.info("Ray Web UI at {}".format(ray.get_webui_url()))
base_experiment_name = f"{CLOUD_PREFIX}learner_{POKER_GAME_VERSION}_sac_arch1_pipeline_psro"
full_experiment_name = f"{base_experiment_name}_{gethostname()}_pid_{os.getpid()}_{datetime_str()}"
experiment_save_dir = os.path.join(DEFAULT_RESULTS_DIR,
full_experiment_name)
def claim_new_active_policy_after_trainer_init_callback(trainer):
def set_train_policy_warmup_target_entropy_proportion(worker):
JOB_STATUS_OPEN = 'open'
JOB_STATUS_ACTIVE = 'active'
def _job_str_for_policy_key(policy_key):
return f"job: {policy_key} {JOB_STATUS_OPEN}"
if __name__ == '__main__':
storage_client = connect_storage_client()
manager_interface = ConsoleManagerInterface(
server_host=MANAGER_SERVER_HOST,
port=MANAGER_PORT,
worker_id=
f"rectified_psro_job_scheduler_{gethostname()}_pid_{os.getpid()}",
storage_client=storage_client,
minio_bucket_name=BUCKET_NAME)
lock_server_interface = LockServerInterface(
server_host=LOCK_SERVER_HOST,
port=LOCK_SERVER_PORT,
worker_id=
f"rectified_psro_job_scheduler_{gethostname()}_pid_{os.getpid()}")
generation_index = 0
while True:
print(f"Starting generation {generation_index}")
active_job_list = []
active_job_policy_keys = []
