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tf_agents.bandits.agents.exp3_mixture_agent.Exp3MixtureAgent

View source on GitHub

An agent that mixes a set of agents and updates the weights with Exp3.

Inherits From: MixtureAgent

tf_agents.bandits.agents.exp3_mixture_agent.Exp3MixtureAgent(
    *args, **kwargs
)

For a reference on EXP3, see Bandit Algorithms by Tor Lattimore and Csaba Szepesvari (https://tor-lattimore.com/downloads/book/book.pdf).

The update uses a slighlty modified version of EXP3 to make sure that the weights do not go to one seemingly good agent in the very beginning. To smooth the weights, two extra measures are taken:

  1. A forgetting factor makes sure that the aggregated reward estimates do not grow indefinitely.
  2. The inverse temperature has a maximum parameter that prevents it from growing indefinitely.

It is generally a good idea to set

forgetting_factor = 1 - (1 / max_inverse_temperature)

so that the two smoothing factors work together nicely.

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_choice_info.

Args:

  • agents: List of TF-Agents agents that this mixture agent trains.
  • variable_collection: An instance of Exp3VariableCollection. If not set, A default one will be created. It contains all the variables that are needed to restore the mixture agent, excluding the variables of the subagents.
  • forgetting: A float value in (0, 1]. This is how much the estimated reward aggregates are shrinked in every training step.
  • max_inverse_temperature: This value caps the inverse temperature that would otherwise grow as the square root of the number of samples seen.
  • name: Name fo this instance of Exp3MixtureAgent.

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_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
)

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.

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.
  • 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.