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Class for representing a trainable constraint using a neural network.
Inherits From: BaseConstraint
tf_agents.bandits.policies.constraints.NeuralConstraint(
time_step_spec: tf_agents.typing.types.TimeStep
,
action_spec: tf_agents.typing.types.BoundedTensorSpec
,
constraint_network: Optional[tf_agents.typing.types.Network
],
error_loss_fn: tf_agents.typing.types.LossFn
= tf.compat.v1.losses.mean_squared_error,
name: Optional[Text] = 'NeuralConstraint'
)
This constraint class uses a neural network to compute the action feasibility. In this case, the loss function needs to be exposed for training the neural network weights, typically done by the agent that uses this constraint.
Args | |
---|---|
time_step_spec
|
A TimeStep spec of the expected time_steps.
|
action_spec
|
A nest of BoundedTensorSpec representing the actions.
|
constraint_network
|
An instance of tf_agents.network.Network used to
provide estimates of action feasibility. The input structure should be
consistent with the observation_spec . If the constraint network is not
available at construction time, it can be set later on using the
constraint_network setter.
|
error_loss_fn
|
A function for computing the loss used to train the
constraint network. The default is tf.losses.mean_squared_error .
|
name
|
Python str name of this agent. All variables in this module will fall under that name. Defaults to the class name. |
Attributes | |
---|---|
constraint_network
|
|
observation_spec
|
Methods
compute_loss
compute_loss(
observations: tf_agents.typing.types.NestedTensor
,
actions: tf_agents.typing.types.NestedTensor
,
rewards: tf_agents.typing.types.Tensor
,
weights: Optional[types.Float] = None,
training: bool = False
) -> tf_agents.typing.types.Tensor
Computes loss for training the constraint network.
Args | |
---|---|
observations
|
A batch of observations. |
actions
|
A batch of actions. |
rewards
|
A batch of rewards. |
weights
|
Optional scalar or elementwise (per-batch-entry) importance weights. The output batch loss will be scaled by these weights, and the final scalar loss is the mean of these values. |
training
|
Whether the loss is being used for training. |
Returns | |
---|---|
loss
|
A Tensor containing the loss for the training step.
|
initialize
initialize()
Returns an op to initialize the constraint.
__call__
__call__(
observation, actions=None
)
Returns the probability of input actions being feasible.