garage.tf.policies.discrete_qf_derived_policy
¶
A Discrete QFunction-derived policy.
This policy chooses the action that yields to the largest Q-value.
-
class
DiscreteQfDerivedPolicy
(env_spec, qf, name='DiscreteQfDerivedPolicy')¶ Bases:
garage.tf.models.Module
,garage.tf.policies.policy.Policy
DiscreteQfDerived policy.
Parameters: - env_spec (garage.envs.env_spec.EnvSpec) – Environment specification.
- qf (garage.q_functions.QFunction) – The q-function used.
- name (str) – Name of the policy.
-
env_spec
¶ Policy environment specification.
Returns: Environment specification. Return type: garage.EnvSpec
-
state_info_specs
¶ State info specification.
Returns: - keys and shapes for the information related to the
- module’s state when taking an action.
Return type: List[str]
-
state_info_keys
¶ State info keys.
Returns: - keys for the information related to the module’s state
- when taking an input.
Return type: List[str]
-
observation_space
¶ Observation space.
Returns: The observation space of the environment. Return type: akro.Space
-
action_space
¶ Action space.
Returns: The action space of the environment. Return type: akro.Space
-
get_action
(self, observation)¶ Get action from this policy for the input observation.
Parameters: observation (numpy.ndarray) – Observation from environment. Returns: Single optimal action from this policy. dict: Predicted action and agent information. It returns an empty dict since there is no parameterization.Return type: numpy.ndarray
-
get_actions
(self, observations)¶ Get actions from this policy for the input observations.
Parameters: observations (numpy.ndarray) – Observations from environment. Returns: Optimal actions from this policy. dict: Predicted action and agent information. It returns an empty dict since there is no parameterization.Return type: numpy.ndarray
-
get_trainable_vars
(self)¶ Get trainable variables.
Returns: - A list of trainable variables in the current
- variable scope.
Return type: List[tf.Variable]
-
get_global_vars
(self)¶ Get global variables.
Returns: - A list of global variables in the current
- variable scope.
Return type: List[tf.Variable]
-
get_regularizable_vars
(self)¶ Get all network weight variables in the current scope.
Returns: - A list of network weight variables in the
- current variable scope.
Return type: List[tf.Variable]
-
get_params
(self)¶ Get the trainable variables.
Returns: - A list of trainable variables in the current
- variable scope.
Return type: List[tf.Variable]
-
get_param_shapes
(self)¶ Get parameter shapes.
Returns: A list of variable shapes. Return type: List[tuple]
-
get_param_values
(self)¶ Get param values.
Returns: - Values of the parameters evaluated in
- the current session
Return type: np.ndarray
-
set_param_values
(self, param_values)¶ Set param values.
Parameters: param_values (np.ndarray) – A numpy array of parameter values.
-
reset
(self, do_resets=None)¶ Reset the module.
This is effective only to recurrent modules. do_resets is effective only to vectoried modules.
For a vectorized modules, do_resets is an array of boolean indicating which internal states to be reset. The length of do_resets should be equal to the length of inputs.
Parameters: do_resets (numpy.ndarray) – Bool array indicating which states to be reset.
-
terminate
(self)¶ Clean up operation.
-
flat_to_params
(self, flattened_params)¶ Unflatten tensors according to their respective shapes.
Parameters: flattened_params (np.ndarray) – A numpy array of flattened params. Returns: - A list of parameters reshaped to the
- shapes specified.
Return type: List[np.ndarray]