def test_huber_loss_config(self):
config_dict = {
'input_layer': 'images',
'output_layer': 'relu_1',
'clip': 0.1,
'weights': 1.0,
'name': 'l',
'collect': False
}
config = HuberLossConfig.from_dict(config_dict)
self.assert_equal_losses(config.to_dict(), config_dict)
def model_fn(features, labels, mode):
model = plx.models.DDQNModel(
mode,
graph_fn=graph_fn,
loss=HuberLossConfig(),
num_states=env.num_states,
num_actions=env.num_actions,
optimizer=SGDConfig(learning_rate=0.01),
exploration_config=DecayExplorationConfig(),
target_update_frequency=10,
summaries='all')
return model(features, labels)
def __init__(self,
mode,
graph_fn,
num_states,
num_actions,
loss=None,
optimizer=None,
metrics=None,
discount=0.97,
exploration_config=None,
use_target_graph=True,
target_update_frequency=5,
is_continuous=False,
dueling='mean',
use_expert_demo=False,
summaries='all',
clip_gradients=0.5,
clip_embed_gradients=0.1,
name="Model"):
self.num_states = num_states
self.num_actions = num_actions
self.exploration_config = exploration_config
self.discount = discount
self.use_target_graph = use_target_graph
self.target_update_frequency = target_update_frequency
self.is_continuous = is_continuous
self.dueling = dueling
self.use_expert_demo = use_expert_demo
loss = loss or HuberLossConfig()
super(BaseQModel,
self).__init__(mode=mode,
name=name,
model_type=self.Types.RL,
graph_fn=graph_fn,
loss=loss,
optimizer=optimizer,
metrics=metrics,
summaries=summaries,
clip_gradients=clip_gradients,
clip_embed_gradients=clip_embed_gradients)
self._train_graph = None
self._target_graph = None