tf_agents.policies.q_policy.QPolicy

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Class to build Q-Policies.

Inherits From: Base

Used in the notebooks

Used in the tutorials

time_step_spec A TimeStep spec of the expected time_steps.
action_spec A nest of BoundedTensorSpec representing the actions.
q_network An instance of a tf_agents.network.Network, callable via network(observation, step_type) -> (output, final_state).
emit_log_probability Whether to emit log-probs in info of PolicyStep.
observation_and_action_constraint_splitter A function used to process observations with action constraints. These constraints can indicate, for example, a mask of valid/invalid actions for a given 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 network and

2) the constraint. An example observation_and_action_constraint_splitter could be as simple as:

def observation_and_action_constraint_splitter(observation):
return observation['network_input'], observation['constraint']

Note: when using observation_and_action_constraint_splitter, make sure the provided q_network is compatible with the network-specific half of the output of the observation_and_action_constraint_splitter. In particular, observation_and_action_constraint_splitter will be called on the observation before passing to the network. If observation_and_action_constraint_splitter is None, action constraints are not applied.

name The name of this policy. All variables in this module will fall under that name. Defaults to the class name.

ValueError If q_network.action_spec exists and is not compatible with action_spec.
NotImplementedError If action_spec contains more than one BoundedTensorSpec.

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.

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.

trajectory_spec Describes the Tensors written when using this policy with an environment.

Methods

action

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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

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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
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

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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

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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

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Returns the list of Variables that belong to the policy.

with_name_scope

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.