garage.tf.policies.gaussian_lstm_policy module

Gaussian LSTM Policy.

A policy represented by a Gaussian distribution which is parameterized by a Long short-term memory (LSTM).

class GaussianLSTMPolicy(env_spec, hidden_dim=32, name='GaussianLSTMPolicy', hidden_nonlinearity=<function tanh>, hidden_w_init=<tensorflow.python.ops.init_ops_v2.GlorotUniform object>, hidden_b_init=<tensorflow.python.ops.init_ops_v2.Zeros object>, recurrent_nonlinearity=<function sigmoid>, recurrent_w_init=<tensorflow.python.ops.init_ops_v2.GlorotUniform object>, output_nonlinearity=None, output_w_init=<tensorflow.python.ops.init_ops_v2.GlorotUniform object>, output_b_init=<tensorflow.python.ops.init_ops_v2.Zeros object>, hidden_state_init=<tensorflow.python.ops.init_ops_v2.Zeros object>, hidden_state_init_trainable=False, cell_state_init=<tensorflow.python.ops.init_ops_v2.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.policy.StochasticPolicy

Gaussian LSTM Policy.

A policy represented by a Gaussian distribution which is parameterized by a Long short-term memory (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).
build(state_input, name=None)[source]

Build policy.

Parameters:
  • state_input (tf.Tensor) – State input.
  • name (str) – Name of the policy, which is also the name scope.
Returns:

Policy distribution. tf.Tensor: Step means, with shape \((N, S^*)\). tf.Tensor: Step log std, with shape \((N, S^*)\). tf.Tensor: Step hidden state, with shape \((N, S^*)\). tf.Tensor: Step cell state, with shape \((N, S^*)\). tf.Tensor: Initial hidden state, with shape \((S^*)\). tf.Tensor: Initial cell state, with shape \((S^*)\)
Return type:

tfp.distributions.MultivariateNormalDiag
clone(name)[source]

Return a clone of the policy.

It only copies the configuration of the primitive, not the parameters.

Parameters:name (str) – Name of the newly created policy. It has to be different from source policy if cloned under the same computational graph.
Returns:Newly cloned policy.
Return type:garage.tf.policies.GaussianLSTMPolicy
distribution

Policy distribution.

Returns:Policy distribution.
Return type:tfp.Distribution.MultivariateNormalDiag
get_action(observation)[source]

Get single action from this policy for the input observation.

Parameters:observation (numpy.ndarray) – Observation from environment.
Returns:Actions dict: Predicted action and agent information.
Return type:numpy.ndarray

Note

It returns an action and a dict, with keys - mean (numpy.ndarray): Mean of the distribution. - log_std (numpy.ndarray): Log standard deviation of the

distribution.
  • prev_action (numpy.ndarray): Previous action, only present if
    self._state_include_action is True.
get_actions(observations)[source]

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.
Return type:numpy.ndarray

Note

It returns an action and a dict, with keys - mean (numpy.ndarray): Means of the distribution. - log_std (numpy.ndarray): Log standard deviations of the

distribution.
  • prev_action (numpy.ndarray): Previous action, only present if
    self._state_include_action is True.
input_dim

Dimension of the policy input.

Type:int
reset(do_resets=None)[source]

Reset the policy.

Note

If do_resets 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:do_resets (numpy.ndarray) – Bool that indicates terminal state(s).
state_info_specs

State info specifcation.

Returns:
keys and shapes for the information related to the
policy’s state when taking an action.
Return type:List[str]
vectorized

Vectorized or not.

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