def __init__(self, actor, critic, num_action, random_process, gamma,
L2_reg, actor_model_dir, critic_model_dir):
self._num_action = num_action
self._gamma = gamma
self._L2_reg = L2_reg
self.global_ts = tf.compat.v1.train.get_or_create_global_step()
self.eval_flg = False
self.actor = actor(num_action)
self.critic = critic(1)
# self.target_actor = deepcopy(self.actor)
# self.target_critic = deepcopy(self.critic)
self.target_actor = actor(num_action)
self.target_critic = critic(1)
self.actor_optimizer = tf.compat.v1.train.AdamOptimizer(
learning_rate=1e-4)
self.critic_optimizer = tf.compat.v1.train.AdamOptimizer(
learning_rate=1e-3)
self.random_process = random_process
self.actor_manager = create_checkpoint(model=self.actor,
optimizer=self.actor_optimizer,
model_dir=actor_model_dir)
self.critic_manager = create_checkpoint(
model=self.critic,
optimizer=self.critic_optimizer,
model_dir=critic_model_dir)
def __init__(self, ggnn, critic, node_info, num_action, params):
self.params = params
self.num_action = num_action
self.eval_flg = False
self.index_timestep = 0
self.actor = ggnn(state_dim=params.num_node_features,
node_info=node_info,
rec_hidden_unit=params.rec_hidden_unit,
rec_output_unit=params.rec_output_unit,
recurrent_step=params.recurrent_step)
self.critic = critic(1)
self.target_actor = deepcopy(self.actor)
self.target_critic = deepcopy(self.critic)
self.actor_optimizer = tf.compat.v1.train.AdamOptimizer(
learning_rate=1e-4)
self.critic_optimizer = tf.compat.v1.train.AdamOptimizer(
learning_rate=1e-3)
self.actor_manager = create_checkpoint(
model=self.actor,
optimizer=self.actor_optimizer,
model_dir=params.actor_model_dir)
self.critic_manager = create_checkpoint(
model=self.critic,
optimizer=self.critic_optimizer,
model_dir=params.critic_model_dir)
def __init__(self, actor, critic, num_action, params):
self.params = params
self.num_action = num_action
self.eval_flg = False
self.index_timestep = 0
self.actor = actor(num_action)
self.critic = critic(1)
self.target_actor = deepcopy(self.actor)
self.target_critic = deepcopy(self.critic)
self.actor_optimizer = tf.train.AdamOptimizer(learning_rate=3e-4) # used as in paper
self.critic_optimizer = tf.train.AdamOptimizer(learning_rate=3e-4) # used as in paper
self.actor_manager = create_checkpoint(model=self.actor,
optimizer=self.actor_optimizer,
model_dir=params.actor_model_dir)
self.critic_manager = create_checkpoint(model=self.critic,
optimizer=self.critic_optimizer,
model_dir=params.critic_model_dir)
def __init__(self, actor, critic, num_action, random_process, params):
self.params = params
self.num_action = num_action
self.eval_flg = False
self.index_timestep = 0
self.actor = actor(num_action)
self.critic = critic(1)
self.target_actor = deepcopy(self.actor)
self.target_critic = deepcopy(self.critic)
self.actor_optimizer = tf.compat.v1.train.AdamOptimizer(
learning_rate=1e-4)
self.critic_optimizer = tf.compat.v1.train.AdamOptimizer(
learning_rate=1e-3)
self.random_process = random_process
self.actor_manager = create_checkpoint(
model=self.actor,
optimizer=self.actor_optimizer,
model_dir=params.actor_model_dir)
self.critic_manager = create_checkpoint(
model=self.critic,
optimizer=self.critic_optimizer,
model_dir=params.critic_model_dir)
def __init__(self, model, optimizer, loss_fn, grad_clip_fn, num_action,
params):
self.params = params
self.num_action = num_action
self.grad_clip_fn = grad_clip_fn
self.loss_fn = loss_fn
self.eval_flg = False
self.index_timestep = 0
self.main_model = model(num_action)
self.target_model = model(num_action)
self.optimizer = optimizer
self.manager = create_checkpoint(model=self.main_model,
optimizer=self.optimizer,
model_dir=params.model_dir)
def __init__(self, model, policy, optimizer, loss_fn, grad_clip_fn,
obs_prc_fn, num_action, model_dir, gamma):
self._gamma = gamma
self._grad_clip_fn = grad_clip_fn
self._loss_fn = loss_fn
self._timestep = 0
self._optimizer = optimizer
self._obs_prc_fn = obs_prc_fn
# === Supposed to access from outside ===
self.policy = policy
self.eval_flg = False
self.num_action = num_action
self.main_model = model(num_action)
self.target_model = model(num_action)
self.manager = create_checkpoint(model=self.main_model,
optimizer=self._optimizer,
model_dir=model_dir)