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tf_agents.bandits.agents.exp3_mixture_agent.Exp3MixtureVariableCollection

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A collection of variables used by subclasses of MixtureAgent.

tf_agents.bandits.agents.exp3_mixture_agent.Exp3MixtureVariableCollection(
    *args, **kwargs
)

Note that this variable collection only contains the mixture weights. The variables of the sub-agents that the mixture agent mixes are in variable collections of the respective sub-agents.

Args:

  • num_agents: (int) the number of agents mixed by the mixture agent.
  • reward_aggregates: A list of floats containing the reward aggregates for each agent. If not set, the initial values will be 0.
  • inverse_temperature: The initial value for the inverse temperature variable used by the mixture agent.

Attributes:

  • inverse_temperature
  • 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.

  • reward_aggregates

  • 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) == []
  • 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

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.