garage.tf.policies.continuous_mlp_policy

This modules creates a continuous MLP policy network.

A continuous MLP network can be used as policy method in different RL algorithms. It accepts an observation of the environment and predicts a continuous action.

class ContinuousMLPPolicy(env_spec, name='ContinuousMLPPolicy', hidden_sizes=(64, 64), hidden_nonlinearity=tf.nn.relu, hidden_w_init=tf.initializers.glorot_uniform(seed=deterministic.get_tf_seed_stream()), hidden_b_init=tf.zeros_initializer(), output_nonlinearity=tf.nn.tanh, output_w_init=tf.initializers.glorot_uniform(seed=deterministic.get_tf_seed_stream()), output_b_init=tf.zeros_initializer(), layer_normalization=False)

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

Inheritance diagram of garage.tf.policies.continuous_mlp_policy.ContinuousMLPPolicy

Continuous MLP Policy Network.

The policy network selects action based on the state of the environment. It uses neural nets to fit the function of pi(s).

Parameters:
  • env_spec (garage.envs.env_spec.EnvSpec) – Environment specification.
  • name (str) – Policy name, also the variable scope.
  • hidden_sizes (list[int]) – Output dimension of dense layer(s). For example, (32, 32) means the MLP of this policy consists of two hidden layers, each with 32 hidden units.
  • 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.
  • 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.
  • layer_normalization (bool) – Bool for using layer normalization or not.
input_dim

Dimension of the policy input.

Type:int
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_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]
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, obs_var, name=None)

Symbolic graph of the action.

Parameters:
  • obs_var (tf.Tensor) – Tensor input for symbolic graph.
  • name (str) – Name for symbolic graph.
Returns:

symbolic graph of the action.
Return type:

tf.Tensor
get_action(self, observation)

Get single action from this policy for the input observation.

Parameters:observation (numpy.ndarray) – Observation from environment.
Returns:Predicted action. dict: Empty dict since this policy does not model a distribution.
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:Predicted actions. dict: Empty dict since this policy does not model a distribution.
Return type:numpy.ndarray
get_regularizable_vars(self)

Get regularizable weight variables under the Policy scope.

Returns:List of regularizable variables.
Return type:list(tf.Variable)
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.
Returns:Clone of this object
Return type:garage.tf.policies.ContinuousMLPPolicy
network_input_spec(self)

Network input spec.

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

Network output spec.

Returns:List of key(str) for the network outputs.
Return type:list[str]
reset(self, do_resets=None)

Reset the module.

This is effective only to recurrent modules. do_resets is effective only to vectoried modules.

For a vectorized modules, 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.

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