garage.tf.policies.gaussian_mlp_policy module

GaussianMLPPolicy with GaussianMLPModel.

class GaussianMLPPolicy(env_spec, name='GaussianMLPPolicy', hidden_sizes=(32, 32), hidden_nonlinearity=<function tanh>, hidden_w_init=<tensorflow.python.ops.init_ops.GlorotUniform object>, hidden_b_init=<tensorflow.python.ops.init_ops.Zeros object>, output_nonlinearity=None, output_w_init=<tensorflow.python.ops.init_ops.GlorotUniform object>, output_b_init=<tensorflow.python.ops.init_ops.Zeros object>, learn_std=True, adaptive_std=False, std_share_network=False, init_std=1.0, min_std=1e-06, max_std=None, std_hidden_sizes=(32, 32), std_hidden_nonlinearity=<function tanh>, std_output_nonlinearity=None, std_parameterization='exp', layer_normalization=False)[source]

Bases: garage.tf.policies.base.StochasticPolicy

GaussianMLPPolicy with GaussianMLPModel.

A policy that contains a MLP to make prediction based on a gaussian distribution.

Parameters:
  • env_spec (garage.envs.env_spec.EnvSpec) – Environment specification.
  • name (str) – Model name, also the variable scope.
  • hidden_sizes (list[int]) – Output dimension of dense layer(s) for the MLP for mean. For example, (32, 32) means the MLP 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.
  • learn_std (bool) – Is std trainable.
  • adaptive_std (bool) – Is std a neural network. If False, it will be a parameter.
  • std_share_network (bool) – Boolean for whether mean and std share the same network.
  • init_std (float) – Initial value for std.
  • std_hidden_sizes (list[int]) – Output dimension of dense layer(s) for the MLP for std. For example, (32, 32) means the MLP consists of two hidden layers, each with 32 hidden units.
  • min_std (float) – If not None, the std is at least the value of min_std, to avoid numerical issues.
  • max_std (float) – If not None, the std is at most the value of max_std, to avoid numerical issues.
  • std_hidden_nonlinearity – Nonlinearity for each hidden layer in the std network.
  • std_output_nonlinearity – Nonlinearity for output layer in the std network.
  • std_parametrization (str) – How the std should be parametrized. There are a few options:
  • exp (-) – the logarithm of the std will be stored, and applied a exponential transformation
  • softplus (-) – the std will be computed as log(1+exp(x))
  • layer_normalization (bool) – Bool for using layer normalization or not.
Returns:
dist_info_sym(obs_var, state_info_vars=None, name='default')[source]

Symbolic graph of the distribution.

distribution

Policy distribution.

get_action(observation)[source]

Get action from the policy.

get_actions(observations)[source]

Get actions from the policy.

get_params(trainable=True)[source]

Get the trainable variables.

vectorized

Vectorized or not.