"""CategoricalMLPPolicy."""
import akro
import tensorflow as tf
from garage.tf.distributions import Categorical
from garage.tf.models import MLPModel
from garage.tf.policies import StochasticPolicy
[docs]class CategoricalMLPPolicy(StochasticPolicy):
"""CategoricalMLPPolicy
A policy that contains a MLP to make prediction based on
a categorical distribution.
It only works with akro.Discrete action space.
Args:
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.
"""
def __init__(self,
env_spec,
name='CategoricalMLPPolicy',
hidden_sizes=(32, 32),
hidden_nonlinearity=tf.nn.tanh,
hidden_w_init=tf.glorot_uniform_initializer(),
hidden_b_init=tf.zeros_initializer(),
output_nonlinearity=tf.nn.softmax,
output_w_init=tf.glorot_uniform_initializer(),
output_b_init=tf.zeros_initializer(),
layer_normalization=False):
assert isinstance(env_spec.action_space, akro.Discrete), (
'CategoricalMLPPolicy only works with akro.Discrete action '
'space.')
super().__init__(name, env_spec)
self.obs_dim = env_spec.observation_space.flat_dim
self.action_dim = env_spec.action_space.n
self.model = MLPModel(output_dim=self.action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
hidden_w_init=hidden_w_init,
hidden_b_init=hidden_b_init,
output_nonlinearity=output_nonlinearity,
output_w_init=output_w_init,
output_b_init=output_b_init,
layer_normalization=layer_normalization,
name='MLPModel')
self._initialize()
def _initialize(self):
state_input = tf.compat.v1.placeholder(tf.float32,
shape=(None, self.obs_dim))
with tf.compat.v1.variable_scope(self.name) as vs:
self._variable_scope = vs
self.model.build(state_input)
self._f_prob = tf.compat.v1.get_default_session().make_callable(
self.model.networks['default'].outputs,
feed_list=[self.model.networks['default'].input])
@property
def vectorized(self):
"""Vectorized or not."""
return True
[docs] def dist_info_sym(self, obs_var, state_info_vars=None, name=None):
"""Symbolic graph of the distribution."""
with tf.compat.v1.variable_scope(self._variable_scope):
prob = self.model.build(obs_var, name=name)
return dict(prob=prob)
[docs] def dist_info(self, obs, state_infos=None):
"""Distribution info."""
prob = self._f_prob(obs)
return dict(prob=prob)
[docs] def get_action(self, observation):
"""Return a single action."""
flat_obs = self.observation_space.flatten(observation)
prob = self._f_prob([flat_obs])[0]
action = self.action_space.weighted_sample(prob)
return action, dict(prob=prob)
[docs] def get_actions(self, observations):
"""Return multiple actions."""
flat_obs = self.observation_space.flatten_n(observations)
probs = self._f_prob(flat_obs)
actions = list(map(self.action_space.weighted_sample, probs))
return actions, dict(prob=probs)
[docs] def get_regularizable_vars(self):
"""Get regularizable weight variables under the Policy scope."""
trainable = self.get_trainable_vars()
return [
var for var in trainable
if 'hidden' in var.name and 'kernel' in var.name
]
@property
def distribution(self):
"""Policy distribution."""
return Categorical(self.action_dim)
def __getstate__(self):
"""Object.__getstate__."""
new_dict = super().__getstate__()
del new_dict['_f_prob']
return new_dict
def __setstate__(self, state):
"""Object.__setstate__."""
super().__setstate__(state)
self._initialize()