"""Categorical GRU Policy.
A policy represented by a Categorical distribution
which is parameterized by a Gated Recurrent Unit (GRU).
"""
# pylint: disable=wrong-import-order
import akro
import numpy as np
import tensorflow as tf
from garage.tf.models import CategoricalGRUModel
from garage.tf.policies.policy import StochasticPolicy
[docs]class CategoricalGRUPolicy(StochasticPolicy):
"""Categorical GRU Policy.
A policy represented by a Categorical distribution
which is parameterized by a Gated Recurrent Unit (GRU).
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_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.
state_include_action (bool): Whether the state includes action.
If True, input dimension will be
(observation dimension + action dimension).
layer_normalization (bool): Bool for using layer normalization or not.
"""
def __init__(self,
env_spec,
name='CategoricalGRUPolicy',
hidden_dim=32,
hidden_nonlinearity=tf.nn.tanh,
hidden_w_init=tf.initializers.glorot_uniform(),
hidden_b_init=tf.zeros_initializer(),
recurrent_nonlinearity=tf.nn.sigmoid,
recurrent_w_init=tf.initializers.glorot_uniform(),
output_nonlinearity=None,
output_w_init=tf.initializers.glorot_uniform(),
output_b_init=tf.zeros_initializer(),
hidden_state_init=tf.zeros_initializer(),
hidden_state_init_trainable=False,
state_include_action=True,
layer_normalization=False):
if not isinstance(env_spec.action_space, akro.Discrete):
raise ValueError('CategoricalGRUPolicy 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._hidden_dim = hidden_dim
self._hidden_nonlinearity = hidden_nonlinearity
self._hidden_w_init = hidden_w_init
self._hidden_b_init = hidden_b_init
self._recurrent_nonlinearity = recurrent_nonlinearity
self._recurrent_w_init = recurrent_w_init
self._output_nonlinearity = output_nonlinearity
self._output_w_init = output_w_init
self._output_b_init = output_b_init
self._hidden_state_init = hidden_state_init
self._hidden_state_init_trainable = hidden_state_init_trainable
self._layer_normalization = layer_normalization
self._state_include_action = state_include_action
if state_include_action:
self._input_dim = self._obs_dim + self._action_dim
else:
self._input_dim = self._obs_dim
self._f_step_prob = None
self.model = CategoricalGRUModel(
output_dim=self._action_dim,
hidden_dim=self._hidden_dim,
name='prob_network',
hidden_nonlinearity=hidden_nonlinearity,
hidden_w_init=hidden_w_init,
hidden_b_init=hidden_b_init,
recurrent_nonlinearity=recurrent_nonlinearity,
recurrent_w_init=recurrent_w_init,
hidden_state_init=hidden_state_init,
hidden_state_init_trainable=hidden_state_init_trainable,
output_nonlinearity=output_nonlinearity,
output_w_init=output_w_init,
output_b_init=output_b_init,
layer_normalization=layer_normalization)
self._prev_actions = None
self._prev_hiddens = None
self._dist = None
self._init_hidden = None
self._initialize()
def _initialize(self):
"""Initialize policy."""
with tf.compat.v1.variable_scope(self.name) as vs:
self._variable_scope = vs
state_input = tf.compat.v1.placeholder(shape=(None, None,
self._input_dim),
name='state_input',
dtype=tf.float32)
step_input_var = tf.compat.v1.placeholder(shape=(None,
self._input_dim),
name='step_input',
dtype=tf.float32)
step_hidden_var = tf.compat.v1.placeholder(
shape=(None, self._hidden_dim),
name='step_hidden_input',
dtype=tf.float32)
(self._dist, step_out, step_hidden,
self._init_hidden) = self.model.build(state_input, step_input_var,
step_hidden_var).outputs
self._f_step_prob = tf.compat.v1.get_default_session().make_callable(
[step_out, step_hidden],
feed_list=[step_input_var, step_hidden_var])
[docs] def build(self, state_input, name=None):
"""Build policy.
Args:
state_input (tf.Tensor) : State input.
name (str): Name of the policy, which is also the name scope.
Returns:
tfp.distributions.OneHotCategorical: Policy distribution.
tf.Tensor: Step output, with shape :math:`(N, S^*)`.
tf.Tensor: Step hidden state, with shape :math:`(N, S^*)`.
tf.Tensor: Initial hidden state , used to reset the hidden state
when policy resets. Shape: :math:`(S^*)`.
"""
with tf.compat.v1.variable_scope(self._variable_scope):
_, step_input_var, step_hidden_var = self.model.inputs
return self.model.build(state_input,
step_input_var,
step_hidden_var,
name=name)
@property
def input_dim(self):
"""int: Dimension of the policy input."""
return self._input_dim
@property
def vectorized(self):
"""Vectorized or not.
Returns:
Bool: True if primitive supports vectorized operations.
"""
return True
[docs] def 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.
Args:
do_resets (numpy.ndarray): Bool that indicates terminal state(s).
"""
if do_resets is None:
do_resets = [True]
do_resets = np.asarray(do_resets)
if self._prev_actions is None or len(do_resets) != len(
self._prev_actions):
self._prev_actions = np.zeros(
(len(do_resets), self.action_space.flat_dim))
self._prev_hiddens = np.zeros((len(do_resets), self._hidden_dim))
self._prev_actions[do_resets] = 0.
self._prev_hiddens[do_resets] = self._init_hidden.eval()
[docs] def get_action(self, observation):
"""Return a single action.
Args:
observation (numpy.ndarray): Observations.
Returns:
int: Action given input observation.
dict(numpy.ndarray): Distribution parameters.
"""
actions, agent_infos = self.get_actions([observation])
return actions[0], {k: v[0] for k, v in agent_infos.items()}
[docs] def get_actions(self, observations):
"""Return multiple actions.
Args:
observations (numpy.ndarray): Observations.
Returns:
list[int]: Actions given input observations.
dict(numpy.ndarray): Distribution parameters.
"""
if self._state_include_action:
assert self._prev_actions is not None
all_input = np.concatenate([observations, self._prev_actions],
axis=-1)
else:
all_input = observations
probs, hidden_vec = self._f_step_prob(all_input, self._prev_hiddens)
actions = list(map(self.action_space.weighted_sample, probs))
prev_actions = self._prev_actions
self._prev_actions = self.action_space.flatten_n(actions)
self._prev_hiddens = hidden_vec
agent_info = dict(prob=probs)
if self._state_include_action:
agent_info['prev_action'] = np.copy(prev_actions)
return actions, agent_info
@property
def distribution(self):
"""Policy distribution.
Returns:
tfp.Distribution.OneHotCategorical: Policy distribution.
"""
return self._dist
@property
def state_info_specs(self):
"""State info specifcation.
Returns:
List[str]: keys and shapes for the information related to the
policy's state when taking an action.
"""
if self._state_include_action:
return [
('prev_action', (self._action_dim, )),
]
return []
[docs] def clone(self, name):
"""Return a clone of the policy.
It only copies the configuration of the primitive,
not the parameters.
Args:
name (str): Name of the newly created policy. It has to be
different from source policy if cloned under the same
computational graph.
Returns:
garage.tf.policies.CategoricalGRUPolicy: Newly cloned policy.
"""
return self.__class__(
name=name,
env_spec=self._env_spec,
hidden_dim=self._hidden_dim,
hidden_nonlinearity=self._hidden_nonlinearity,
hidden_w_init=self._hidden_w_init,
hidden_b_init=self._hidden_b_init,
recurrent_nonlinearity=self._recurrent_nonlinearity,
recurrent_w_init=self._recurrent_w_init,
output_nonlinearity=self._output_nonlinearity,
output_w_init=self._output_w_init,
output_b_init=self._output_b_init,
hidden_state_init=self._hidden_state_init,
hidden_state_init_trainable=self._hidden_state_init_trainable,
state_include_action=self._state_include_action,
layer_normalization=self._layer_normalization)
def __getstate__(self):
"""Object.__getstate__.
Returns:
dict: the state to be pickled for the instance.
"""
new_dict = super().__getstate__()
del new_dict['_f_step_prob']
del new_dict['_dist']
del new_dict['_init_hidden']
return new_dict
def __setstate__(self, state):
"""Object.__setstate__.
Args:
state (dict): Unpickled state.
"""
super().__setstate__(state)
self._initialize()