def __init__(self, settings):
self.settings = update_settings(DEFAULT_SETTINGS, settings)
# network and training
self.q_network = parse_block(settings["model"])
self.optimizer = parse_optimizer(settings["optimizer"])
out_sh = self.q_network.output_shape()
assert len(out_sh) == 2 and out_sh[0] is None, \
"Output of the Discrete DeepQ must be (None, num_actions), where None corresponds to batch_size"
self.num_actions = out_sh[1]
self.minipatch_size = self.settings["minibatch_size"]
self.train_every_nth = self.settings['train_every_nth']
self.discount_rate = self.settings["discount_rate"]
self.transitions_so_far = 0
self.exploration_period = self.settings['exploration_period']
self.random_action_probability = self.settings['random_action_probability']
self.replay_buffer = deque()
self.store_every_nth = self.settings['store_every_nth']
self.replay_buffer_size = self.settings['replay_buffer_size']
self.target_network_update_rate = self.settings['target_network_update_rate']
self.summary_writer = None
self.s = tf.Session()
self.create_variables()
self.s.run(tf.initialize_variables(
self.q_network.variables() + self.target_q_network.variables()))
def make_apply_gradients_fun(settings, model):
#### CREATE ALL THE OPTIMIZERS
optimizers = {
name:parse_optimizer(settings['model']['settings']['optimizers'][name])
for name in settings['model']['settings']['optimizers']
}
update_ops = []
for var, grad in zip(model.variables(), model.gradients()):
var_optimizer = None
for optimizer_name, optimizer in optimizers.items():
if var.name.startswith(optimizer_name):
var_optimizer = optimizer
break
if optimizer is None:
raise Exception("Could not match optimizer for variable %s" % (var.name))
update_op = optimizer.apply_gradients([(grad.value(), var)])
update_ops.append(update_op)
combined_update_op = tf.group(*update_ops)
return lambda: model.get_session().run(combined_update_op)
def __init__(self, settings):
self.settings = update_settings(DEFAULT_SETTINGS, settings)
# network and training
self.q_network = parse_block(settings["model"])
self.optimizer = parse_optimizer(settings["optimizer"])
out_sh = self.q_network.output_shape()
assert len(out_sh) == 2 and out_sh[0] is None, \
"Output of the Discrete DeepQ must be (None, num_actions), where None corresponds to batch_size"
self.num_actions = out_sh[1]
self.minipatch_size = self.settings["minibatch_size"]
self.train_every_nth = self.settings['train_every_nth']
self.discount_rate = self.settings["discount_rate"]
self.transitions_so_far = 0
self.exploration_period = self.settings['exploration_period']
self.random_action_probability = self.settings[
'random_action_probability']
self.replay_buffer = deque()
self.store_every_nth = self.settings['store_every_nth']
self.replay_buffer_size = self.settings['replay_buffer_size']
self.target_network_update_rate = self.settings[
'target_network_update_rate']
self.summary_writer = None
self.s = tf.Session()
self.create_variables()
self.s.run(
tf.initialize_variables(self.q_network.variables() +
self.target_q_network.variables()))