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Parallel agents fixes (#95)

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* Parallel agents related bug fixes: checkpoint restore, tensorboard integration.
Adding narrow networks support.
Reference code for unlimited number of checkpoints
This commit is contained in:
Itai Caspi
2018-05-24 14:24:19 +03:00
committed by GitHub
parent 6c0b59b4de
commit d302168c8c
10 changed files with 75 additions and 41 deletions
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20
architectures/tensorflow_components/general_network.py
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@@ -36,6 +36,7 @@ class GeneralTensorFlowNetwork(TensorFlowArchitecture):
self.output_heads = []
self.activation_function = self.get_activation_function(
tuning_parameters.agent.hidden_layers_activation_function)
self.embedder_width = tuning_parameters.agent.embedder_width
TensorFlowArchitecture.__init__(self, tuning_parameters, name, global_network, network_is_local)
@@ -57,22 +58,26 @@ class GeneralTensorFlowNetwork(TensorFlowArchitecture):
def get_observation_embedding(with_timestep=False):
if self.input_height > 1:
return ImageEmbedder((self.input_height, self.input_width, self.input_depth), name="observation",
input_rescaler=self.tp.agent.input_rescaler)
input_rescaler=self.tp.agent.input_rescaler, embedder_width=self.embedder_width)
else:
return VectorEmbedder((self.input_width + int(with_timestep), self.input_depth), name="observation")
return VectorEmbedder((self.input_width + int(with_timestep), self.input_depth), name="observation",
embedder_width=self.embedder_width)
input_mapping = {
InputTypes.Observation: get_observation_embedding(),
InputTypes.Measurements: VectorEmbedder(self.measurements_size, name="measurements"),
InputTypes.GoalVector: VectorEmbedder(self.measurements_size, name="goal_vector"),
InputTypes.Action: VectorEmbedder((self.num_actions,), name="action"),
InputTypes.Measurements: VectorEmbedder(self.measurements_size, name="measurements",
embedder_width=self.embedder_width),
InputTypes.GoalVector: VectorEmbedder(self.measurements_size, name="goal_vector",
embedder_width=self.embedder_width),
InputTypes.Action: VectorEmbedder((self.num_actions,), name="action",
embedder_width=self.embedder_width),
InputTypes.TimedObservation: get_observation_embedding(with_timestep=True),
}
return input_mapping[embedder_type]
def get_middleware_embedder(self, middleware_type):
return {MiddlewareTypes.LSTM: LSTM_Embedder,
MiddlewareTypes.FC: FC_Embedder}.get(middleware_type)(self.activation_function)
MiddlewareTypes.FC: FC_Embedder}.get(middleware_type)(self.activation_function, self.embedder_width)
def get_output_head(self, head_type, head_idx, loss_weight=1.):
output_mapping = {
@@ -174,7 +179,8 @@ class GeneralTensorFlowNetwork(TensorFlowArchitecture):
self.losses = tf.losses.get_losses(self.name)
self.losses += tf.losses.get_regularization_losses(self.name)
self.total_loss = tf.losses.compute_weighted_loss(self.losses, scope=self.name)
tf.summary.scalar('total_loss', self.total_loss)
if self.tp.visualization.tensorboard:
tf.summary.scalar('total_loss', self.total_loss)
# Learning rate