tf_agents.bandits.agents.linear_thompson_sampling_agent.LinearThompsonSamplingAgent

Linear Thompson Sampling Agent.

Inherits From: LinearBanditAgent, TFAgent

tf_agents.bandits.agents.linear_thompson_sampling_agent.LinearThompsonSamplingAgent(
    time_step_spec: tf_agents.typing.types.TimeStep,
    action_spec: tf_agents.typing.types.BoundedTensorSpec,
    variable_collection: Optional[tf_agents.bandits.agents.linear_bandit_agent.LinearBanditVariableCollection] = None,
    alpha: float = 1.0,
    gamma: float = 1.0,
    use_eigendecomp: bool = False,
    tikhonov_weight: float = 1.0,
    add_bias: bool = False,
    emit_policy_info: Sequence[Text] = (),
    observation_and_action_constraint_splitter: Optional[types.Splitter] = None,
    accepts_per_arm_features: bool = False,
    debug_summaries: bool = False,
    summarize_grads_and_vars: bool = False,
    enable_summaries: bool = True,
    dtype: tf.DType = tf.float32,
    name: Optional[Text] = None
)

Implements the Linear Thompson Sampling Agent from the following paper: "Thompson Sampling for Contextual Bandits with Linear Payoffs", Shipra Agrawal, Navin Goyal, ICML 2013. The actual algorithm implemented is Algorithm 3 from the supplementary material of the paper from <a href="http://proceedings.mlr.press/v28/agrawal13-supp.pdf">http://proceedings.mlr.press/v28/agrawal13-supp.pdf</a>.

In a nutshell, the agent maintains two parameters weight_covariances and parameter_estimators, and updates them based on experience. The inverse of the weight covariance parameters are updated with the outer product of the observations using the Woodbury inverse matrix update, while the parameter estimators are updated by the reward-weighted observation vectors for every action.

time_step_spec A TimeStep spec describing the expected TimeSteps.
action_spec A scalar BoundedTensorSpec with int32 or int64 dtype describing the number of actions for this agent.
variable_collection Instance of LinearBanditVariableCollection. Collection of variables to be updated by the agent. If None, a new instance of LinearBanditVariableCollection will be created.
alpha (float) positive scalar. This is the exploration parameter that multiplies the confidence intervals.
gamma a float forgetting factor in [0.0, 1.0]. When set to 1.0, the algorithm does not forget.
use_eigendecomp whether to use eigen-decomposition or not. The default solver is Conjugate Gradient.
tikhonov_weight (float) tikhonov regularization term.
add_bias If true, a bias term will be added to the linear reward estimation.
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.
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.
accepts_per_arm_features (bool) Whether the agent accepts per-arm features.
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.
dtype The type of the parameters stored and updated by the agent. Should be one of tf.float32 and tf.float64. Defaults to tf.float32.
name a name for this instance of LinearThompsonSamplingAgent.

ValueError if dtype is not one of tf.float32 or tf.float64.

action_spec TensorSpec describing the action produced by the agent.
alpha

collect_data_context

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.
cov_matrix

data_context

data_vector

debug_summaries

eig_matrix

eig_vals

num_actions

num_samples

policy Return the current policy held by the agent.
summaries_enabled

summarize_grads_and_vars

theta Returns the matrix of per-arm feature weights.

The returned matrix has shape (num_actions, context_dim). It's equivalent to a stacking of theta vectors from the paper.

time_step_spec Describes the TimeStep tensors expected by the agent.
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.

Methods

compute_summaries

View source

compute_summaries(
    loss: tf_agents.typing.types.Tensor
)

initialize

View source

initialize() -> Optional[tf.Operation]

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

View source

loss(
    experience: tf_agents.typing.types.NestedTensor,
    weights: Optional[types.Tensor] = None,
    training: bool = False,
    **kwargs
) -> tf_agents.agents.tf_agent.LossInfo

Gets loss from the agent.

If the user calls this from _train, it must be in a tf.GradientTape scope in order to apply gradients to trainable variables. If intermediate gradient steps are needed, _loss and _train will return different values since _loss only supports updating all gradients at once after all losses have been calculated.

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.
training Explicit argument to pass to loss. This typically affects network computation paths like dropout and batch normalization.
**kwargs Any additional data as args to loss.

Returns
A LossInfo loss tuple containing loss and info tensors.

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

post_process_policy

View source

post_process_policy() -> tf_agents.policies.TFPolicy

Post process policies after training.

The policies of some agents require expensive post processing after training before they can be used. e.g. A Recommender agent might require rebuilding an index of actions. For such agents, this method will return a post processed version of the policy. The post processing may either update the existing policies in place or create a new policy, depnding on the agent. The default implementation for agents that do not want to override this method is to return agent.policy.

Returns
The post processed policy.

preprocess_sequence

View source

preprocess_sequence(
    experience: tf_agents.typing.types.NestedTensor
) -> tf_agents.typing.types.NestedTensor

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.

train

View source

train(
    experience: tf_agents.typing.types.NestedTensor,
    weights: Optional[types.Tensor] = None,
    **kwargs
) -> tf_agents.agents.tf_agent.LossInfo

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 to pass to the subclass.

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

update_alpha

View source

update_alpha(
    alpha
)