def __init__(self,
action_size,
observation_size,
lr=1e-5,
learning_rate_decay_steps=1000,
learning_rate_decay_rate=0.95,
training_param=TrainingParam()):
BaseDeepQ.__init__(self, action_size, observation_size, lr,
learning_rate_decay_steps, learning_rate_decay_rate,
training_param)
# TODO add as meta param the number of "Q" you want to use (here 2)
# TODO add as meta param size and types of the networks
self.average_reward = 0
self.life_spent = 1
self.qvalue_evolution = np.zeros((0, ))
self.Is_nan = False
self.model_value_target = None
self.model_value = None
self.model_Q = None
self.model_Q2 = None
self.model_policy = None
self.construct_q_network()
self.previous_size = 0
self.previous_eyes = None
self.previous_arange = None
self.previous_size_train = 0
self.previous_eyes_train = None
def __init__(self, nn_params, training_param=None, verbose=False):
if training_param is None:
training_param = TrainingParam()
BaseDeepQ.__init__(self, nn_params, training_param, verbose=verbose)
# TODO add as meta param the number of "Q" you want to use (here 2)
# TODO add as meta param size and types of the networks
self.average_reward = 0
self.life_spent = 1
self.qvalue_evolution = np.zeros((0, ))
self.Is_nan = False
self.model_value_target = None
self.model_value = None
self.model_Q = None
self.model_Q2 = None
self.model_policy = None
self.construct_q_network()
self.previous_size = 0
self.previous_eyes = None
self.previous_arange = None
self.previous_size_train = 0
self.previous_eyes_train = None
# optimizers and learning rate
self.schedule_lr_policy = None
self.optimizer_policy = None
self.schedule_lr_Q = None
self.optimizer_Q = None
self.schedule_lr_Q2 = None
self.optimizer_Q2 = None
self.schedule_lr_value = None
self.optimizer_value = None
def __init__(self, nn_params, training_param=None):
if training_param is None:
training_param = TrainingParam()
BaseDeepQ.__init__(self, nn_params, training_param)
self._custom_objects = {"LtauBis": LtauBis}
self.construct_q_network()
self._max_global_norm_grad = training_param.max_global_norm_grad
self._max_value_grad = training_param.max_value_grad
self._max_loss = training_param.max_loss
def __init__(self,
nn_params,
training_param=None):
if training_param is None:
training_param = TrainingParam()
BaseDeepQ.__init__(self,
nn_params,
training_param)
self.schedule_lr_model = None
self.construct_q_network()
def __init__(self,
action_size,
observation_size,
lr=1e-5,
learning_rate_decay_steps=1000,
learning_rate_decay_rate=0.95,
training_param=TrainingParam()):
BaseDeepQ.__init__(self, action_size, observation_size, lr,
learning_rate_decay_steps, learning_rate_decay_rate,
training_param)
self.construct_q_network()
def __init__(self, nn_params, training_param=None):
if training_param is None:
training_param = TrainingParam()
BaseDeepQ.__init__(self, nn_params, training_param)
self._custom_objects = {"LtauBis": LtauBis}
self._max_global_norm_grad = training_param.max_global_norm_grad
self._max_value_grad = training_param.max_value_grad
self._max_loss = training_param.max_loss
self.train_lr = 1.0
# added
self.encoded_state = None
self.grid_model = None
self._schedule_grid_model = None
self._optimizer_grid_model = None
self._qnet_variables = []
self.grid_model_losses_npy = None
self.construct_q_network()
def __init__(self,
action_size,
observation_size,
tau_dim_start,
tau_dim_end,
add_tau,
lr=0.00001,
learning_rate_decay_steps=1000,
learning_rate_decay_rate=0.95,
training_param=TrainingParam()):
BaseDeepQ.__init__(self,
action_size,
observation_size,
lr,
learning_rate_decay_steps=learning_rate_decay_steps,
learning_rate_decay_rate=learning_rate_decay_rate,
training_param=training_param)
self.tau_dim_start = tau_dim_start
self.tau_dim_end = tau_dim_end
self.add_tau = add_tau
self.custom_objects = {"Ltau": Ltau}
self.construct_q_network()