def __init__(self, model_info, alg_config, **kwargs):
"""
Initialize DQN algorithm.
It contains four steps:
1. override the default config, with user's configuration;
2. create the default actor with Algorithm.__init__;
3. create once more actor, named by target_actor;
4. create the replay buffer for training.
:param model_info:
:param alg_config:
"""
model_info = model_info["actor"]
super(DQNInfoFlowAlg, self).__init__(alg_name="info_flow_dqn",
model_info=model_info,
alg_config=alg_config)
self.target_actor = model_builder(model_info)
self.buff = ReplayBuffer(alg_config.get("buffer_size", BUFFER_SIZE))
self.batch_size = alg_config.get("batch_size", BATCH_SIZE)
self.target_update_freq = alg_config.get("target_update_freq",
TARGET_UPDATE_FREQ)
self.gamma = alg_config.get("gamma", GAMMA)
self.item_dim = alg_config.get("item_dim")
self.user_dim = alg_config.get("user_dim")
self.async_flag = False
self._times = list()
def __init__(self, alg_name, model_info, alg_config=None, **kwargs):
"""
use the model info create a algorithm
:param alg_name:
:param model_info: model_info["actor"]
:param alg_config:
"""
self.actor = model_builder(model_info)
self.state_dim = model_info.get("state_dim")
self.action_dim = model_info.get("action_dim")
self.train_count = 0
self.alg_name = alg_name
self.alg_config = alg_config
self.async_flag = True
# set default weights map, make compatibility to single agent
self._weights_map = self.update_weights_map()
# trainable state
self._train_ready = True
self.sync_weights = False
# train property
self._prepare_times_per_train = alg_config.get(
"prepare_times_per_train",
alg_config["instance_num"] * alg_config["agent_num"],
)
self.dist_model_policy = DefaultAlgDistPolicy(
alg_config["instance_num"],
prepare_times=self._prepare_times_per_train)
self.learning_starts = alg_config.get("learning_starts", 0)
self._train_per_checkpoint = alg_config.get("train_per_checkpoint", 1)
logging.debug("train/checkpoint: {}".format(self.train_per_checkpoint))
def __init__(self, model_info, alg_config, **kwargs):
"""Initialize DQN algorithm. it's contains four steps:
1. override the default config, with user's configuration;
2. create the default actor with Algorithm.__init__;
3. create once more actor, named by target_actor;
4. create the replay buffer for training.
:param model_info:
:param alg_config:
"""
import_config(globals(), alg_config)
model_info = model_info["actor"]
super(DQN, self).__init__(alg_name="dqn",
model_info=model_info,
alg_config=alg_config)
self.target_actor = model_builder(model_info)
self.buff = ReplayBuffer(BUFFER_SIZE)