tf_agents.bandits.policies.neural_linucb_policy.NeuralLinUCBPolicy

Neural LinUCB Policy.

Inherits From: Base

tf_agents.bandits.policies.neural_linucb_policy.NeuralLinUCBPolicy(
    encoding_network, encoding_dim, reward_layer, epsilon_greedy,
    actions_from_reward_layer, cov_matrix, data_vector, num_samples,
    time_step_spec=None, alpha=1.0, emit_policy_info=(), emit_log_probability=False,
    observation_and_action_constraint_splitter=None, name=None
)

Applies LinUCB on top of an encoding network. Since LinUCB is a linear method, the encoding network is used to capture the non-linear relationship between the context features and the expected rewards. The policy starts with exploration based on epsilon greedy and then switches to LinUCB for exploring more efficiently.

Reference:

Carlos Riquelme, George Tucker, Jasper Snoek, Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling, ICLR 2018.

Args
`encoding_network`	network that encodes the observations.
`encoding_dim`	(int) dimension of the encoded observations.
`reward_layer`	final layer that predicts the expected reward per arm.
`epsilon_greedy`	(float) representing the probability of choosing a random action instead of the greedy action.
`actions_from_reward_layer`	(bool) whether to get actions from the reward layer or from LinUCB.
`cov_matrix`	list of the covariance matrices. There exists one covariance matrix per arm.
`data_vector`	list of the data vectors. A data vector is a weighted sum of the observations, where the weight is the corresponding reward. Each arm has its own data vector.
`num_samples`	list of number of samples per arm.
`time_step_spec`	A `TimeStep` spec of the expected time_steps.
`alpha`	(float) non-negative weight multiplying the confidence intervals.
`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`.
`emit_log_probability`	(bool) whether to emit log probabilities.
`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 policy and 2) the mask. The mask should be a 0-1 `Tensor` of shape `[batch_size, num_actions]`. This function should also work with a `TensorSpec` as input, and should output `TensorSpec` objects for the observation and mask.
`name`	The name of this policy.

Attributes
`action_spec`	Describes the TensorSpecs of the Tensors expected by `step(action)`. `action` can be a single Tensor, or a nested dict, list or tuple of Tensors.
`collect_data_spec`	Describes the Tensors written when using this policy with an environment.
`emit_log_probability`	Whether this policy instance emits log probabilities or not.
`info_spec`	Describes the Tensors emitted as info by `action` and `distribution`. `info` can be an empty tuple, a single Tensor, or a nested dict, list or tuple of Tensors.
`name`	Returns the name of this module as passed or determined in the ctor. Note: This is not the same as the `self.name_scope.name` which includes parent module names.
`name_scope`	Returns a `tf.name_scope` instance for this class.
`observation_and_action_constraint_splitter`
`policy_state_spec`	Describes the Tensors expected by `step(_, policy_state)`. `policy_state` can be an empty tuple, a single Tensor, or a nested dict, list or tuple of Tensors.
`policy_step_spec`	Describes the output of `action()`.
`submodules`	Sequence of all sub-modules. Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on). `a = tf.Module()` `b = tf.Module()` `c = tf.Module()` `a.b = b` `b.c = c` `list(a.submodules) == [b, c]` `True` `list(b.submodules) == [c]` `True` `list(c.submodules) == []` `True`
`time_step_spec`	Describes the `TimeStep` tensors returned by `step()`.
`trainable_variables`	Sequence of trainable variables owned by this module and its submodules. Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don't expect the return value to change.
`trajectory_spec`	Describes the Tensors written when using this policy with an environment.

Methods

`action`

View source

action(
    time_step, policy_state=(), seed=None
)

Generates next action given the time_step and policy_state.

Args
`time_step`	A `TimeStep` tuple corresponding to `time_step_spec()`.
`policy_state`	A Tensor, or a nested dict, list or tuple of Tensors representing the previous policy_state.
`seed`	Seed to use if action performs sampling (optional).

Returns
A `PolicyStep` named tuple containing: `action`: An action Tensor matching the `action_spec()`. `state`: A policy state tensor to be fed into the next call to action. `info`: Optional side information such as action log probabilities.

Raises
`RuntimeError`	If subclass init didn't call super().init.

`distribution`

View source

distribution(
    time_step, policy_state=()
)

Generates the distribution over next actions given the time_step.

Args
`time_step`	A `TimeStep` tuple corresponding to `time_step_spec()`.
`policy_state`	A Tensor, or a nested dict, list or tuple of Tensors representing the previous policy_state.

Returns

Returns
A `PolicyStep` named tuple containing: `action`: A tf.distribution capturing the distribution of next actions. `state`: A policy state tensor for the next call to distribution. `info`: Optional side information such as action log probabilities.

A PolicyStep named tuple containing:

action: A tf.distribution capturing the distribution of next actions. state: A policy state tensor for the next call to distribution. info: Optional side information such as action log probabilities.

`get_initial_state`

View source

get_initial_state(
    batch_size
)

Returns an initial state usable by the policy.

Args
`batch_size`	Tensor or constant: size of the batch dimension. Can be None in which case not dimensions gets added.

Returns
A nested object of type `policy_state` containing properly initialized Tensors.

`update`

View source

update(
    policy, tau=1.0, tau_non_trainable=None, sort_variables_by_name=False
)

Update the current policy with another policy.

This would include copying the variables from the other policy.

Args
`policy`	Another policy it can update from.
`tau`	A float scalar in [0, 1]. When tau is 1.0 (the default), we do a hard update. This is used for trainable variables.
`tau_non_trainable`	A float scalar in [0, 1] for non_trainable variables. If None, will copy from tau.
`sort_variables_by_name`	A bool, when True would sort the variables by name before doing the update.

Returns
An TF op to do the update.

`variables`

View source

variables()

Returns the list of Variables that belong to the policy.

`with_name_scope`

@classmethod
with_name_scope(
    method
)

Decorator to automatically enter the module name scope.

class MyModule(tf.Module):
  @tf.Module.with_name_scope
  def __call__(self, x):
    if not hasattr(self, 'w'):
      self.w = tf.Variable(tf.random.normal([x.shape[1], 3]))
    return tf.matmul(x, self.w)

Using the above module would produce tf.Variables and tf.Tensors whose names included the module name:

mod = MyModule()
mod(tf.ones([1, 2]))
<tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)>
mod.w
<tf.Variable 'my_module/Variable:0' shape=(2, 3) dtype=float32,
numpy=..., dtype=float32)>

Args
`method`	The method to wrap.

Returns
The original method wrapped such that it enters the module's name scope.

tf_agents.bandits.policies.neural_linucb_policy.NeuralLinUCBPolicy

Reference:

Args

Attributes

Methods

action

distribution

get_initial_state

update

variables

with_name_scope

`action`

`distribution`

`get_initial_state`

`update`

`variables`

`with_name_scope`