tf_agents.policies.categorical_q_policy.CategoricalQPolicy

Class to build categorical Q-policies.

Inherits From: TFPolicy

time_step_spec A TimeStep spec of the expected time_steps.
action_spec A BoundedTensorSpec representing the actions.
q_network A network.Network to use for our policy.
min_q_value A float specifying the minimum Q-value, used for setting up the support.
max_q_value A float specifying the maximum Q-value, used for setting up the support.
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.

temperature temperature for sampling, when close to 0.0 is arg_max.

ValueError if q_network does not have property num_atoms.
TypeError if action_spec is not a 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.

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().
time_step_spec Describes the TimeStep tensors returned by step().
trajectory_spec Describes the Tensors written when using this policy with an environment.
validate_args Whether action & distribution validate input and output args.

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. ValueError or TypeError: If validate_args is True and inputs or outputs do not match time_step_spec, policy_state_spec, or policy_step_spec.

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.

Raises
ValueError or TypeError: If validate_args is True and inputs or outputs do not match time_step_spec, policy_state_spec, or policy_step_spec.

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 no 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.