garage.tf.policies.discrete_qf_argmax_policy¶
A Discrete QFunction-derived policy.
This policy chooses the action that yields to the largest Q-value.
- class DiscreteQFArgmaxPolicy(env_spec, qf, name='DiscreteQFArgmaxPolicy')¶
Bases:
garage.tf.models.Module,garage.tf.policies.policy.Policy
DiscreteQFArgmax 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.
- property env_spec¶
Policy environment specification.
- Returns
Environment specification.
- Return type
- property 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]
- property 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]
- property observation_space¶
Observation space.
- Returns
The observation space of the environment.
- Return type
akro.Space
- property action_space¶
Action space.
- Returns
The action space of the environment.
- Return type
akro.Space
- get_action(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(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()¶
Get trainable variables.
- Returns
- A list of trainable variables in the current
variable scope.
- Return type
List[tf.Variable]
- get_global_vars()¶
Get global variables.
- Returns
- A list of global variables in the current
variable scope.
- Return type
List[tf.Variable]
- get_regularizable_vars()¶
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()¶
Get the trainable variables.
- Returns
- A list of trainable variables in the current
variable scope.
- Return type
List[tf.Variable]
- get_param_shapes()¶
Get parameter shapes.
- Returns
A list of variable shapes.
- Return type
List[tuple]
- get_param_values()¶
Get param values.
- Returns
- Values of the parameters evaluated in
the current session
- Return type
np.ndarray
- set_param_values(param_values)¶
Set param values.
- Parameters
param_values (np.ndarray) – A numpy array of parameter values.
- reset(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()¶
Clean up operation.
- flat_to_params(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]