garage.tf.policies.categorical_lstm_policy

Categorical LSTM Policy.

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

class CategoricalLSTMPolicy(env_spec, name='CategoricalLSTMPolicy', hidden_dim=32, hidden_nonlinearity=tf.nn.tanh, hidden_w_init=tf.initializers.glorot_uniform(seed=deterministic.get_tf_seed_stream()), hidden_b_init=tf.zeros_initializer(), recurrent_nonlinearity=tf.nn.sigmoid, recurrent_w_init=tf.initializers.glorot_uniform(seed=deterministic.get_tf_seed_stream()), output_nonlinearity=tf.nn.softmax, output_w_init=tf.initializers.glorot_uniform(seed=deterministic.get_tf_seed_stream()), output_b_init=tf.zeros_initializer(), hidden_state_init=tf.zeros_initializer(), hidden_state_init_trainable=False, cell_state_init=tf.zeros_initializer(), cell_state_init_trainable=False, state_include_action=True, forget_bias=True, layer_normalization=False)

Bases: garage.tf.models.CategoricalLSTMModel, garage.tf.policies.policy.Policy

Categorical LSTM Policy.

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

It only works with akro.Discrete action space.

Parameters:

• env_spec (garage.envs.env_spec.EnvSpec) – Environment specification.
• name (str) – Policy name, also the variable scope.
• hidden_dim (int) – Hidden dimension for LSTM cell.
• 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.
• state_include_action (bool) – Whether the state includes action. If True, input dimension will be (observation dimension + action dimension).
• 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.
• layer_normalization (bool) – Bool for using layer normalization or not.

input_dim

Dimension of the policy input.

Type:int

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]

env_spec

Policy environment specification.

Returns:Environment specification. Return type:garage.EnvSpec

parameters

Parameters of the model.

Returns:Parameters Return type:np.ndarray

name

Name (str) of the model.

This is also the variable scope of the model.

Returns:Name of the model. Return type:str

input

Default input of the model.

When the model is built the first time, by default it creates the ‘default’ network. This property creates a reference to the input of the network.

Returns:Default input of the model. Return type:tf.Tensor

output

Default output of the model.

When the model is built the first time, by default it creates the ‘default’ network. This property creates a reference to the output of the network.

Returns:Default output of the model. Return type:tf.Tensor

inputs

Default inputs of the model.

When the model is built the first time, by default it creates the ‘default’ network. This property creates a reference to the inputs of the network.

Returns:Default inputs of the model. Return type:list[tf.Tensor]

outputs

Default outputs of the model.

When the model is built the first time, by default it creates the ‘default’ network. This property creates a reference to the outputs of the network.

Returns:Default outputs of the model. Return type:list[tf.Tensor]

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]

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

build(self, state_input, name=None)

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 output, 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, used to reset the hidden state

when policy resets. Shape: \((S^*)\)

tf.Tensor: Initial cell state, used to reset the cell state

when policy resets. Shape: \((S^*)\)

Return type:

tfp.distributions.OneHotCategorical

reset(self, do_resets=None)

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).

get_action(self, observation)

Return a single action.

Parameters:observation (numpy.ndarray) – Observations. Returns:Action given input observation. dict(numpy.ndarray): Distribution parameters. Return type:int

get_actions(self, observations)

Return multiple actions.

Parameters:observations (numpy.ndarray) – Observations. Returns:Actions given input observations. dict(numpy.ndarray): Distribution parameters. Return type:list[int]

clone(self, name)

Return a clone of the policy.

It copies the configuration of the primitive and also 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.CategoricalLSTMPolicy

network_output_spec(self)

Network output spec.

Returns:Name of the model outputs, in order. Return type:list[str]

network_input_spec(self)

Network input spec.

Returns:List of key(str) for the network outputs. Return type:list[str]

terminate(self)

Clean up operation.

get_trainable_vars(self)

Get trainable variables.

Returns:

A list of trainable variables in the current

variable scope.

Return type:List[tf.Variable]

get_global_vars(self)

Get global variables.

Returns:

A list of global variables in the current

variable scope.

Return type:List[tf.Variable]

get_regularizable_vars(self)

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(self)

Get the trainable variables.

Returns:

A list of trainable variables in the current

variable scope.

Return type:List[tf.Variable]

get_param_shapes(self)

Get parameter shapes.

Returns:A list of variable shapes. Return type:List[tuple]

get_param_values(self)

Get param values.

Returns:

Values of the parameters evaluated in

the current session

Return type:np.ndarray

set_param_values(self, param_values)

Set param values.

Parameters:param_values (np.ndarray) – A numpy array of parameter values.

flat_to_params(self, 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]