garage.tf.policies.gaussian_lstm_policy module

GaussianLSTMPolicy with GaussianLSTMModel.

class GaussianLSTMPolicy(env_spec, hidden_dim=32, name='GaussianLSTMPolicy', hidden_nonlinearity=<function tanh>, hidden_w_init=<tensorflow.python.ops.init_ops.GlorotUniform object>, hidden_b_init=<tensorflow.python.ops.init_ops.Zeros object>, recurrent_nonlinearity=<function sigmoid>, recurrent_w_init=<tensorflow.python.ops.init_ops.GlorotUniform object>, output_nonlinearity=None, output_w_init=<tensorflow.python.ops.init_ops.GlorotUniform object>, output_b_init=<tensorflow.python.ops.init_ops.Zeros object>, hidden_state_init=<tensorflow.python.ops.init_ops.Zeros object>, hidden_state_init_trainable=False, cell_state_init=<tensorflow.python.ops.init_ops.Zeros object>, cell_state_init_trainable=False, forget_bias=True, learn_std=True, std_share_network=False, init_std=1.0, layer_normalization=False, state_include_action=True)[source]

Bases: garage.tf.policies.base.StochasticPolicy

A policy which models actions with a Gaussian parameterized by an LSTM.

Parameters:
  • env_spec (garage.envs.env_spec.EnvSpec) – Environment specification.
  • name (str) – Model name, also the variable scope.
  • hidden_dim (int) – Hidden dimension for LSTM cell for mean.
  • hidden_nonlinearity (Callable) – Activation function for intermediate dense layer(s). It should return a tf.Tensor. Set it to None to maintain a linear activation.
  • hidden_w_init (Callable) – Initializer function for the weight of intermediate dense layer(s). The function should return a tf.Tensor.
  • hidden_b_init (Callable) – Initializer function for the bias of intermediate dense layer(s). The function should return a tf.Tensor.
  • recurrent_nonlinearity (Callable) – Activation function for recurrent layers. It should return a tf.Tensor. Set it to None to maintain a linear activation.
  • recurrent_w_init (Callable) – Initializer function for the weight of recurrent layer(s). The function should return a tf.Tensor.
  • output_nonlinearity (Callable) – Activation function for output dense layer. It should return a tf.Tensor. Set it to None to maintain a linear activation.
  • output_w_init (Callable) – Initializer function for the weight of output dense layer(s). The function should return a tf.Tensor.
  • output_b_init (Callable) – Initializer function for the bias of output dense layer(s). The function should return a tf.Tensor.
  • hidden_state_init (Callable) – Initializer function for the initial hidden state. The functino should return a tf.Tensor.
  • hidden_state_init_trainable (bool) – Bool for whether the initial hidden state is trainable.
  • cell_state_init (Callable) – Initializer function for the initial cell state. The functino should return a tf.Tensor.
  • cell_state_init_trainable (bool) – Bool for whether the initial cell state is trainable.
  • forget_bias (bool) – If True, add 1 to the bias of the forget gate at initialization. It’s used to reduce the scale of forgetting at the beginning of the training.
  • learn_std (bool) – Is std trainable.
  • std_share_network (bool) – Boolean for whether mean and std share the same network.
  • init_std (float) – Initial value for std.
  • layer_normalization (bool) – Bool for using layer normalization or not.
  • state_include_action (bool) – Whether the state includes action. If True, input dimension will be (observation dimension + action dimension).
dist_info_sym(obs_var, state_info_vars, name=None)[source]

Build a symbolic graph of the action distribution parameters.

Parameters:
  • obs_var (tf.Tensor) – Tensor input for symbolic graph.
  • state_info_vars (dict) – Extra state information, e.g. previous action.
  • name (str) – Name for symbolic graph.
Returns:

Output of the symbolic graph of action

distribution parameters.
Return type:

dict[tf.Tensor]
distribution

Policy distribution.

Type:garage.tf.distributions.DiagonalGaussian
get_action(observation)[source]

Get single action from this policy for the input observation.

Parameters:observation (numpy.ndarray) – Observation from environment.
Returns:Predicted action and agent information.
action (numpy.ndarray): Predicted action. agent_info (dict): Distribution obtained after observing the
given observation, with keys * mean: (numpy.ndarray) * log_std: (numpy.ndarray) * prev_action: (numpy.ndarray), only present if
self._state_include_action is True.
Return type:tuple[numpy.ndarray, dict]
get_actions(observations)[source]

Get multiple actions from this policy for the input observations.

Parameters:observations (numpy.ndarray) – Observations from environment.
Returns:Predicted action and agent information.
actions (numpy.ndarray): Predicted actions. agent_infos (dict): Distribution obtained after observing the
given observation, with keys * mean: (numpy.ndarray) * log_std: (numpy.ndarray) * prev_action: (numpy.ndarray), only present if
self._state_include_action is True.
Return type:tuple[numpy.ndarray, dict]
recurrent

Whether this policy is recurrent or not.

Type:bool
reset(dones=None)[source]

Reset the policy.

Note

If dones is None, it will be by default np.array([True]), which implies the policy will not be “vectorized”, i.e. number of paralle environments for training data sampling = 1.

Parameters:dones (numpy.ndarray) – Bool that indicates terminal state(s).
state_info_specs

State info specification.

Type:list
vectorized

Whether this policy is vectorized.

Type:bool