tf_agents.bandits.agents.dropout_thompson_sampling_agent.DropoutThompsonSamplingAgent

A neural network based Thompson sampling agent.

Inherits From: GreedyRewardPredictionAgent, TFAgent

This agent receives parameters for a neural network and trains it to predict rewards. The action is chosen greedily with respect to the prediction. The neural network implements dropout for exploration.

time_step_spec A TimeStep spec of the expected time_steps.
action_spec A nest of BoundedTensorSpec representing the actions.
optimizer The optimizer to use for training.
dropout_rate Float in (0, 1), the dropout rate.
network_layers Tuple of ints determining the sizes of the network layers.
dropout_only_top_layer Boolean parameter determining if dropout should be done only in the top layer. True by default.
observation_and_action_constraint_splitter A function used for masking valid/invalid actions with each state of the environment. The function takes in a full observation and returns a tuple consisting of 1) the part of the observation intended as input to the bandit agent and policy, and 2) the boolean mask. This function should also work with a TensorSpec as input, and should output TensorSpec objects for the observation and mask.
constraints iterable of constraints objects that are instances of tf_agents.bandits.agents.NeuralConstraint.
error_loss_fn A function for computing the error loss, taking parameters labels, predictions, and weights (any function from tf.losses would work). The default is tf.losses.mean_squared_error.
gradient_clipping A float representing the norm length to clip gradients (or None for no clipping.)
heteroscedastic If True, the variance per action is estimated and the losses are weighted appropriately.
debug_summaries A Python bool, default False. When True, debug summaries are gathered.
summarize_grads_and_vars A Python bool, default False. When True, gradients and network variable summaries are written during training.
enable_summaries A Python bool, default True. When False, all summaries (debug or otherwise) should not be written.
emit_policy_info (tuple of strings) what side information we want to get as part of the policy info. Allowed values can be found in policy_utilities.PolicyInfo.
train_step_counter An optional tf.Variable to increment every time the train op is run. Defaults to the global_step.
laplacian_matrix A float Tensor shaped [num_actions, num_actions]. This holds the Laplacian matrix used to regularize the smoothness of the estimated expected reward function. This only applies to problems where the actions have a graph structure. If None, the regularization is not applied.
laplacian_smoothing_weight A float that determines the weight of the regularization term. Note that this has no effect if laplacian_matrix above is None.
name Python str name of this agent. All variables in this module will fall under that name. Defaults to the class name.

ValueError If the action spec contains more than one action or or it is not a bounded scalar int32 spec with minimum 0.

action_spec TensorSpec describing the action produced by the agent.
collect_data_spec Returns a Trajectory spec, as expected by the collect_policy.
collect_policy Return a policy that can be used to collect data from the environment.
data_context

debug_summaries

policy Return the current policy held by the agent.
summaries_enabled

summarize_grads_and_vars

time_step_spec Describes the TimeStep tensors expected by the agent.
train_argspec TensorSpec describing extra supported kwargs to train().
train_sequence_length The number of time steps needed in experience tensors passed to train.

Train requires experience to be a Trajectory containing tensors shaped [B, T, ...]. This argument describes the value of T required.

For example, for non-RNN DQN training, T=2 because DQN requires single transitions.

If this value is None, then train can handle an unknown T (it can be determined at runtime from the data). Most RNN-based agents fall into this category.

train_step_counter

training_data_spec Returns a trajectory spec, as expected by the train() function.
validate_args Whether train & preprocess_sequence validate input & output args.

Methods

compute_summaries

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initialize

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Initializes the agent.

Returns
An operation that can be used to initialize the agent.

Raises
RuntimeError If the class was not initialized properly (super.__init__ was not called).

loss

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Computes loss for training the reward and constraint networks.

Args
observations A batch of observations.
actions A batch of actions.
rewards A batch of rewards. In the case we have constraints, we assume that reward is a dict of tensors with 'reward' and 'constraint' keys defined in 'bandit_spec_utils'. In case of many constraint signals, the constraint tensor has many columns; one column per constraint signal.
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 LossInfo containing the loss for the training step.

Raises
ValueError if the number of actions is greater than 1.

preprocess_sequence

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Defines preprocess_sequence function to be fed into replay buffers.

This defines how we preprocess the collected data before training. Defaults to pass through for most agents. Structure of experience must match that of self.collect_data_spec.

Args
experience a Trajectory shaped [batch, time, ...] or [time, ...] which represents the collected experience data.

Returns
A post processed Trajectory with the same shape as the input.

Raises
TypeError If experience does not match self.collect_data_spec structure types.

reward_loss

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Computes loss for reward prediction training.

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.

Raises
ValueError if the number of actions is greater than 1.

train

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Trains the agent.

Args
experience A batch of experience data in the form of a Trajectory. The structure of experience must match that of self.training_data_spec. All tensors in experience must be shaped [batch, time, ...] where time must be equal to self.train_step_length if that property is not None.
weights (optional). A Tensor, either 0-D or shaped [batch], containing weights to be used when calculating the total train loss. Weights are typically multiplied elementwise against the per-batch loss, but the implementation is up to the Agent.
**kwargs Any additional data as declared by self.train_argspec.

Returns
A LossInfo loss tuple containing loss and info tensors.

  • In eager mode, the loss values are first calculated, then a train step is performed before they are returned.
  • In graph mode, executing any or all of the loss tensors will first calculate the loss value(s), then perform a train step, and return the pre-train-step LossInfo.

Raises
TypeError If validate_args is True and: Experience is not type Trajectory; or if experience does not match self.training_data_spec structure types.
ValueError If validate_args is True and: Experience tensors' time axes are not compatible with self.train_sequence_length; or if experience does not match self.training_data_spec structure.
ValueError If validate_args is True and the user does not pass **kwargs matching self.train_argspec.
RuntimeError If the class was not initialized properly (super.__init__ was not called).