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tf_agents.bandits.agents.static_mixture_agent.StaticMixtureAgent

View source on GitHub

An agent that mixes a set of agents with a given static mixture.

Inherits From: MixtureAgent

tf_agents.bandits.agents.static_mixture_agent.StaticMixtureAgent(
    *args, **kwargs
)

For every data sample, the agent updates the sub-agent that was used to make the action choice in that sample. For this update to happen, the mixture agent needs to have the information on which sub-agent is "responsible" for the action. This information is in a policy info field mixture_agent_id.

Note that this agent makes use of tf.dynamic_partition, and thus it is not compatible with XLA.

Args:

  • mixture_distribution: An instance of tfd.Categorical distribution. This distribution is used to draw sub-policies by the mixture policy. The parameters of the distribution is trained by the mixture agent.
  • agents: List of instances of TF-Agents bandit agents. These agents will be trained and used to select actions. The length of this list should match that of mixture_weights.
  • name: The name of this instance of MixtureAgent.

Attributes:

  • 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.

  • debug_summaries

  • 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.

  • policy: Return the current policy held by the agent.

  • 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
assert list(a.submodules) == [b, c]
assert list(b.submodules) == [c]
assert list(c.submodules) == []
  • 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

  • trainable_variables: Sequence of trainable variables owned by this module and its submodules.

  • variables: Sequence of variables owned by this module and its submodules.

Methods

initialize

View source

initialize()

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).

train

View source

train(
    experience, weights=None, **kwargs
)

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.collect_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 experience is not type Trajectory. Or if experience does not match self.collect_data_spec structure types.
  • ValueError: If experience tensors' time axes are not compatible with self.train_sequence_length. Or if experience does not match self.collect_data_spec structure.
  • ValueError: If the user does not pass **kwargs matching self.train_argspec.
  • RuntimeError: If the class was not initialized properly (super.__init__ was not called).

with_name_scope

@classmethod
with_name_scope(
    cls, 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], 64]))
    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([8, 32]))
# ==> <tf.Tensor: ...>
mod.w
# ==> <tf.Variable ...'my_module/w:0'>

Args:

  • method: The method to wrap.

Returns:

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