def _build_graph(self, network):
self.feature_net = FeatureNet(self.state_shape, network,
self.special.get("feature_get", None))
self.hidden_state = LinearHiddenState(
self.feature_net.feature_state,
self.special.get("hidden_size", 512),
self.special.get("hidden_activation", tf.nn.elu))
self.policy_net = PolicyNet(self.hidden_state.state, self.n_actions,
self.special.get("policy_net", None))
self.value_net = ValueNet(self.hidden_state.state,
self.special.get("value_net", None))
build_model_optimization(
self.policy_net, self.special.get("policy_net_optimization", None))
build_model_optimization(
self.value_net, self.special.get("value_net_optimization", None))
build_model_optimization(
self.hidden_state,
self.special.get("hidden_state_optimization", None),
loss=0.5 * (self.policy_net.loss + self.value_net.loss))
build_model_optimization(
self.feature_net,
self.special.get("feature_net_optimization", None),
loss=0.5 * (self.policy_net.loss + self.value_net.loss))
def _build_graph(self, network):
self.feature_net = FeatureNet(
self.state_shape, network,
self.special.get("feature_net", {}))
self.hidden_state = LinearHiddenState(
self.feature_net.feature_state,
self.special.get("hidden_size", 512),
self.special.get("hidden_activation", tf.nn.elu))
if self.special.get("dueling_network", False):
self.qvalue_net = QvalueNet(
self.hidden_state.state, self.n_actions,
dict(**self.special.get("qvalue_net", {}), **{"advantage": True}))
self.value_net = ValueNet(
self.hidden_state.state,
self.special.get("value_net", {}))
# a bit hacky way
self.predicted_qvalues = self.value_net.predicted_values + \
self.qvalue_net.predicted_qvalues
self.predicted_qvalues_for_action = self.value_net.predicted_values_for_actions + \
self.qvalue_net.predicted_qvalues_for_actions
self.agent_loss = tf.losses.mean_squared_error(
labels=self.qvalue_net.td_target,
predictions=self.predicted_qvalues_for_action)
build_model_optimization(
self.value_net,
self.special.get("value_net_optimization", None),
loss=self.agent_loss)
else:
self.qvalue_net = QvalueNet(
self.hidden_state.state, self.n_actions,
self.special.get("qvalue_net", {}))
self.predicted_qvalues = self.qvalue_net.predicted_qvalues
self.predicted_qvalues_for_action = self.qvalue_net.predicted_qvalues_for_actions
self.agent_loss = self.qvalue_net.loss
build_model_optimization(
self.qvalue_net,
self.special.get("qvalue_net_optimization", None))
build_model_optimization(
self.hidden_state,
self.special.get("hidden_state_optimization", None),
loss=self.agent_loss)
build_model_optimization(
self.feature_net,
self.special.get("feature_net_optimization", None),
loss=self.agent_loss)
def __init__(self,
vocab_size, embedding_size,
encoder_args, decoder_args,
embeddings_optimization_args=None,
encoder_optimization_args=None,
decoder_optimization_args=None):
self.embeddings = Embeddings(
vocab_size,
embedding_size,
scope="embeddings")
self.encoder = DynamicRnnEncoder(
embedding_matrix=self.embeddings.embedding_matrix,
**encoder_args)
self.decoder = DynamicRnnDecoder(
encoder_state=self.encoder.state,
encoder_outputs=self.encoder.outputs,
encoder_inputs_length=self.encoder.inputs_length,
embedding_matrix=self.embeddings.embedding_matrix,
**decoder_args)
build_model_optimization(self.encoder, encoder_optimization_args, self.decoder.loss)
build_model_optimization(self.decoder, decoder_optimization_args)
build_model_optimization(self.embeddings, embeddings_optimization_args, self.decoder.loss)