def start(self):
""" run evaluator """
_ag = AgentGroup(self.env_para, self.alg_para, self.agent_para)
while True:
recv_data = self.recv_broker.get()
cmd = get_msg_info(recv_data, "cmd")
logging.debug("evaluator get meg: {}".format(recv_data))
if cmd not in ["eval"]:
continue
model_name = get_msg_data(recv_data)
_ag.restore(model_name) # fixme: load weight 'file' from the disk
eval_data = _ag.evaluate(self.bm_eval.get("episodes_per_eval", 1))
# return each rewards for each agent
record_item = tuple([eval_data, model_name])
print_immediately("collect eval results: {}".format(record_item))
record_item = message(
record_item,
cmd="eval_result",
broker_id=self.broker_id,
test_id=self.test_id,
)
self.send_broker.send(record_item)
def start(self):
"""Run evaluator."""
setproctitle.setproctitle("xt_evaluator")
_ags = AgentGroup(self.env_para,
self.alg_para,
self.agent_para,
scene="evaluate")
while True:
recv_data = self.recv_broker.get()
cmd = get_msg_info(recv_data, "cmd")
logging.debug("evaluator get meg: {}".format(type(recv_data)))
if cmd in ("close", ):
break
if cmd not in ["eval"]:
print_immediately("eval get un-used data:{}".format(recv_data))
continue
# print_immediately("recv_data in evaluator: {}".format(
# [v.keys() for v in recv_data["data"].values()]))
for train_count, weights in recv_data["data"].items():
_ags.restore(weights, is_id=False)
eval_data = _ags.evaluate(
self.bm_eval.get("episodes_per_eval", 1))
# return each rewards for each agent
record_item = tuple([
eval_data, {
"train_count": train_count,
"broker_id": self.broker_id,
"test_id": self.test_id
}
])
print_immediately(
"collect eval results: {}".format(record_item))
record_item = message(
record_item,
cmd="eval_return",
broker_id=self.broker_id,
test_id=self.test_id,
)
self.send_broker.send(record_item)