garage.tf.policies.uniform_control_policy

Uniform control policy.

class UniformControlPolicy(env_spec)

Bases: garage.tf.policies.policy.Policy

Inheritance diagram of garage.tf.policies.uniform_control_policy.UniformControlPolicy

Policy that output random action uniformly.

Parameters:env_spec (garage.envs.env_spec.EnvSpec) – Environment specification.
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]
name

Name of policy.

Returns:Name of policy
Return type:str
env_spec

Policy environment specification.

Returns:Environment specification.
Return type:garage.EnvSpec
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 single action from this policy for the input observation.

Parameters:observation (numpy.ndarray) – Observation from environment.
Returns:Action 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 multiple actions from this policy for the input observations.

Parameters:observations (numpy.ndarray) – Observations from environment.
Returns:Actions dict: Predicted action and agent information. It returns an empty
dict since there is no parameterization.
Return type:numpy.ndarray
reset(self, do_resets=None)

Reset the policy.

This is effective only to recurrent policies.

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, i.e. batch size.

Parameters:do_resets (numpy.ndarray) – Bool array indicating which states to be reset.