# Path: agent/evolution_agent.py
# class evolutionary_agent(base_agent, parallel_rollout_master_agent):
#     def __init__(self, args, task_q, result_q, agent_id,
#                  name_scope='evolutionary_agent'):
#     def rollout_and_train(self):
#     def ask_for_rollouts(self):
#     def rollout_for_one_episode(self):
#     def act(self, obs):
#     def build_env(self, received_data=None):
#     def run(self):
#     def build_models(self, received_data=None):
#     def build_ppo_update_op(self):
#     def update_ppo_parameters(self, paths):
#     def update_adaptive_hyperparams(self, kl_epoch, i_kl_total):
#     def restore_all(self):
#     def get_output_path(self, save=True):
#     def prepared_network_feeddict(self, rollout_data):
#     def construct_minibatchFeeddict_from_feeddict(self,
#                                                   feed_dict,
#                                                   minibatch_id_candidate,
#                                                   current_id,
#                                                   batch_size,
#                                                   is_all_feed=False,
#                                                   nervenetplus_batch_pos=None):
#     def prepare_feed_dict_map(self):
#     def get_species_info(self, training_stats=None, received_data=None):
# 
# Path: util/parallel_util.py
# START_SIGNAL = 1
# END_SIGNAL = -1
# END_ROLLOUT_SIGNAL = -2
# AGENT_COLLECT_FILTER_INFO = 23
# AGENT_SYNCHRONIZE_FILTER = 233
# AGENT_SET_POLICY_WEIGHTS = 2333
# AGENT_EVOLUTION_TRAIN = 23333
# AGENT_EVOLUTION_START = 233333


import multiprocessing
from agent import evolution_agent
from util import parallel_util
            ['start_time', 'end_time']
            ['env_xml', 'topology_data']
            ['parent_species']
    '''

    def __init__(self, num_agent, args):
        self.args = args
        self.task_queue = multiprocessing.JoinableQueue()
        self.result_queue = multiprocessing.Queue()

        # make the agents
        self.agents_dict = dict()
        for i_agent in range(num_agent):
            agent = evolution_agent.evolutionary_agent(
                args, self.task_queue, self.result_queue, i_agent
            )
            agent.start()
            self.agents_dict[i_agent] = (agent)

    def put(self, task):
        self.task_queue.put(task)

    def join(self):
        self.task_queue.join()

    def get(self):
        results = self.result_queue.get()

        agent_id = results['agent_id']
        del self.agents_dict[agent_id]

# Path: agent/evolution_agent.py
# class evolutionary_agent(base_agent, parallel_rollout_master_agent):
#     def __init__(self, args, task_q, result_q, agent_id,
#                  name_scope='evolutionary_agent'):
#     def rollout_and_train(self):
#     def ask_for_rollouts(self):
#     def rollout_for_one_episode(self):
#     def act(self, obs):
#     def build_env(self, received_data=None):
#     def run(self):
#     def build_models(self, received_data=None):
#     def build_ppo_update_op(self):
#     def update_ppo_parameters(self, paths):
#     def update_adaptive_hyperparams(self, kl_epoch, i_kl_total):
#     def restore_all(self):
#     def get_output_path(self, save=True):
#     def prepared_network_feeddict(self, rollout_data):
#     def construct_minibatchFeeddict_from_feeddict(self,
#                                                   feed_dict,
#                                                   minibatch_id_candidate,
#                                                   current_id,
#                                                   batch_size,
#                                                   is_all_feed=False,
#                                                   nervenetplus_batch_pos=None):
#     def prepare_feed_dict_map(self):
#     def get_species_info(self, training_stats=None, received_data=None):
# 
# Path: util/parallel_util.py
# START_SIGNAL = 1
# END_SIGNAL = -1
# END_ROLLOUT_SIGNAL = -2
# AGENT_COLLECT_FILTER_INFO = 23
# AGENT_SYNCHRONIZE_FILTER = 233
# AGENT_SET_POLICY_WEIGHTS = 2333
# AGENT_EVOLUTION_TRAIN = 23333
# AGENT_EVOLUTION_START = 233333


# Path: evolution/agent_bank.py
import multiprocessing
from agent import evolution_agent
from util import parallel_util

            ['start_time', 'end_time']
            ['env_xml', 'topology_data']
            ['parent_species']
    '''

    def __init__(self, num_agent, args):
        self.args = args
        self.task_queue = multiprocessing.JoinableQueue()
        self.result_queue = multiprocessing.Queue()

        # make the agents
        self.agents_dict = dict()
        for i_agent in range(num_agent):
            agent = evolution_agent.evolutionary_agent(
                args, self.task_queue, self.result_queue, i_agent
            )
            agent.start()
            self.agents_dict[i_agent] = (agent)

    def put(self, task):
        self.task_queue.put(task)

    def join(self):
        self.task_queue.join()

    def get(self):
        results = self.result_queue.get()

        agent_id = results['agent_id']
        del self.agents_dict[agent_id]

        agent = evolution_agent.evolutionary_agent(