garage.tf.policies.discrete_qf_derived_policy module¶

A Discrete QFunction-derived policy.

This policy chooses the action that yields to the largest Q-value.

class DiscreteQfDerivedPolicy(env_spec, qf, name='DiscreteQfDerivedPolicy')[source]¶

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.

get_action(observation)[source]¶

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)[source]¶

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

vectorized¶

Vectorized or not.

Returns:	True if primitive supports vectorized operations.
Return type:	Bool