garage.tf.policies.categorical_mlp_policy module

CategoricalMLPPolicy.

class CategoricalMLPPolicy(env_spec, name='CategoricalMLPPolicy', 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=<function softmax>, output_w_init=<tensorflow.python.ops.init_ops.GlorotUniform object>, output_b_init=<tensorflow.python.ops.init_ops.Zeros object>, layer_normalization=False)

Bases: garage.tf.policies.base.StochasticPolicy

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

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_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.

dist_info(obs, state_infos=None)

Distribution info.

dist_info_sym(obs_var, state_info_vars=None, name=None)

Symbolic graph of the distribution.

distribution

Policy distribution.

get_action(observation)

Return a single action.

get_actions(observations)

Return multiple actions.

get_regularizable_vars()

Get regularizable weight variables under the Policy scope.

vectorized

Vectorized or not.