def test_output_values(self, output_dim, hidden_dim):
model = LSTMModel(
output_dim=output_dim,
hidden_dim=hidden_dim,
hidden_nonlinearity=None,
recurrent_nonlinearity=None,
hidden_w_init=tf.constant_initializer(1),
recurrent_w_init=tf.constant_initializer(1),
output_w_init=tf.constant_initializer(1))
step_hidden_var = tf.placeholder(
shape=(self.batch_size, hidden_dim),
name='step_hidden',
dtype=tf.float32)
step_cell_var = tf.placeholder(
shape=(self.batch_size, hidden_dim),
name='step_cell',
dtype=tf.float32)
outputs = model.build(self._input_var, self._step_input_var,
step_hidden_var, step_cell_var)
output = self.sess.run(
outputs[0], feed_dict={self._input_var: self.obs_inputs})
expected_output = np.full(
[self.batch_size, self.time_step, output_dim], hidden_dim * 8)
assert np.array_equal(output, expected_output)
def __init__(self,
env_spec,
name='CategoricalLSTMPolicyWithModel',
hidden_dim=32,
hidden_nonlinearity=tf.nn.tanh,
hidden_w_init=tf.glorot_uniform_initializer(),
hidden_b_init=tf.zeros_initializer(),
recurrent_nonlinearity=tf.nn.sigmoid,
recurrent_w_init=tf.glorot_uniform_initializer(),
output_nonlinearity=tf.nn.softmax,
output_w_init=tf.glorot_uniform_initializer(),
output_b_init=tf.zeros_initializer(),
hidden_state_init=tf.zeros_initializer(),
hidden_state_init_trainable=False,
cell_state_init=tf.zeros_initializer(),
cell_state_init_trainable=False,
state_include_action=True,
forget_bias=True,
layer_normalization=False):
if not isinstance(env_spec.action_space, Discrete):
raise ValueError('CategoricalLSTMPolicy only works'
'with akro.tf.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._state_include_action = state_include_action
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._cell_state_init = cell_state_init
if state_include_action:
self._input_dim = self._obs_dim + self._action_dim
else:
self._input_dim = self._obs_dim
self.model = LSTMModel(
output_dim=self._action_dim,
hidden_dim=self._hidden_dim,
name='prob_network',
forget_bias=forget_bias,
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,
cell_state_init=cell_state_init,
cell_state_init_trainable=cell_state_init_trainable,
output_nonlinearity=output_nonlinearity,
output_w_init=output_w_init,
output_b_init=output_b_init,
layer_normalization=layer_normalization)
self._initialize()
class CategoricalLSTMPolicy(StochasticPolicy):
"""Estimate action distribution with Categorical parameterized by a LSTM.
A policy that contains a LSTM 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_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.
cell_state_init (callable): Initializer function for the
initial cell state. The functino should return a tf.Tensor.
cell_state_init_trainable (bool): Bool for whether the initial
cell state is trainable.
state_include_action (bool): Whether the state includes action.
If True, input dimension will be
(observation dimension + action dimension).
forget_bias (bool): If True, add 1 to the bias of the forget gate
at initialization. It's used to reduce the scale of forgetting at
the beginning of the training.
layer_normalization (bool): Bool for using layer normalization or not.
"""
def __init__(self,
env_spec,
name='CategoricalLSTMPolicy',
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=tf.nn.softmax,
output_w_init=tf.initializers.glorot_uniform(),
output_b_init=tf.zeros_initializer(),
hidden_state_init=tf.zeros_initializer(),
hidden_state_init_trainable=False,
cell_state_init=tf.zeros_initializer(),
cell_state_init_trainable=False,
state_include_action=True,
forget_bias=True,
layer_normalization=False):
if not isinstance(env_spec.action_space, akro.Discrete):
raise ValueError('CategoricalLSTMPolicy 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._state_include_action = state_include_action
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._cell_state_init = cell_state_init
self._cell_stat_init_trainable = cell_state_init_trainable
self._forget_bias = forget_bias
self._layer_normalization = layer_normalization
if state_include_action:
self._input_dim = self._obs_dim + self._action_dim
else:
self._input_dim = self._obs_dim
self.model = LSTMModel(
output_dim=self._action_dim,
hidden_dim=self._hidden_dim,
name='prob_network',
forget_bias=forget_bias,
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,
cell_state_init=cell_state_init,
cell_state_init_trainable=cell_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._prev_cells = None
self._initialize()
def _initialize(self):
"""Initialize model."""
obs_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, None, self._input_dim))
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)
step_cell_var = tf.compat.v1.placeholder(shape=(None,
self._hidden_dim),
name='step_cell_input',
dtype=tf.float32)
with tf.compat.v1.variable_scope(self.name) as vs:
self._variable_scope = vs
self.model.build(obs_ph, step_input_var, step_hidden_var,
step_cell_var)
self._f_step_prob = tf.compat.v1.get_default_session().make_callable(
[
self.model.networks['default'].step_output,
self.model.networks['default'].step_hidden,
self.model.networks['default'].step_cell
],
feed_list=[step_input_var, step_hidden_var, step_cell_var])
@property
def vectorized(self):
"""Vectorized or not.
Returns:
bool: True if primitive supports vectorized operations.
"""
return True
def dist_info_sym(self, obs_var, state_info_vars, name=None):
"""Build a symbolic graph of the distribution parameters.
Args:
obs_var (tf.Tensor): Tensor input for symbolic graph.
state_info_vars (dict[np.ndarray]): Extra state information, e.g.
previous action.
name (str): Name for symbolic graph.
Returns:
dict[tf.Tensor]: Outputs of the symbolic graph of distribution
parameters.
"""
if self._state_include_action:
prev_action_var = state_info_vars['prev_action']
prev_action_var = tf.cast(prev_action_var, tf.float32)
all_input_var = tf.concat(axis=2,
values=[obs_var, prev_action_var])
else:
all_input_var = obs_var
with tf.compat.v1.variable_scope(self._variable_scope):
outputs, _, _, _, _, _ = self.model.build(
all_input_var,
self.model.networks['default'].step_input,
self.model.networks['default'].step_hidden_input,
self.model.networks['default'].step_cell_input,
name=name)
return dict(prob=outputs)
def reset(self, dones=None):
"""Reset the policy.
Note:
If `dones` is None, it will be by default `np.array([True])` which
implies the policy will not be "vectorized", i.e. number of
parallel environments for training data sampling = 1.
Args:
dones (numpy.ndarray): Bool that indicates terminal state(s).
"""
if dones is None:
dones = [True]
dones = np.asarray(dones)
if self._prev_actions is None or len(dones) != len(self._prev_actions):
self._prev_actions = np.zeros(
(len(dones), self.action_space.flat_dim))
self._prev_hiddens = np.zeros((len(dones), self._hidden_dim))
self._prev_cells = np.zeros((len(dones), self._hidden_dim))
self._prev_actions[dones] = 0.
self._prev_hiddens[dones] = self.model.networks[
'default'].init_hidden.eval()
self._prev_cells[dones] = self.model.networks[
'default'].init_cell.eval()
def get_action(self, observation):
"""Get single action from this policy for the input observation.
Args:
observation (numpy.ndarray): Observation from environment.
Returns:
numpy.ndarray: Predicted action.
dict[str: np.ndarray]: Action distribution.
"""
actions, agent_infos = self.get_actions([observation])
return actions[0], {k: v[0] for k, v in agent_infos.items()}
def get_actions(self, observations):
"""Get multiple actions from this policy for the input observations.
Args:
observations (numpy.ndarray): Observations from environment.
Returns:
numpy.ndarray: Predicted actions.
dict[str: np.ndarray]: Action distributions.
"""
flat_obs = self.observation_space.flatten_n(observations)
if self._state_include_action:
assert self._prev_actions is not None
all_input = np.concatenate([flat_obs, self._prev_actions], axis=-1)
else:
all_input = flat_obs
probs, hidden_vec, cell_vec = self._f_step_prob(
all_input, self._prev_hiddens, self._prev_cells)
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
self._prev_cells = cell_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 recurrent(self):
"""Recurrent or not.
Returns:
bool: True if policy is recurrent.
"""
return True
@property
def distribution(self):
"""Policy distribution.
Returns:
garage.tf.distributions.DiagonalGaussian: Policy distribution.
"""
return RecurrentCategorical(self._action_dim)
@property
def state_info_specs(self):
"""State info specification.
Returns:
list[tuple]: State info specification.
"""
if self._state_include_action:
return [
('prev_action', (self._action_dim, )),
]
else:
return []
def clone(self, name):
"""Return a clone of the policy.
It only copies the configuration of the Q-function,
not the parameters.
Args:
name (str): Name of the newly created policy.
Returns:
garage.tf.policies.CategoricalLSTMPolicy: Clone of this object
"""
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,
cell_state_init=self._cell_state_init,
cell_state_init_trainable=self._cell_stat_init_trainable,
state_include_action=self._state_include_action,
forget_bias=self._forget_bias,
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']
return new_dict
def __setstate__(self, state):
"""Object.__setstate__.
Args:
state (dict): Unpickled state.
"""
super().__setstate__(state)
self._initialize()
class CategoricalLSTMPolicyWithModel(StochasticPolicy2):
"""
CategoricalLSTMPolicy with model.
A policy that contains a LSTM to make prediction based on
a categorical distribution.
It only works with akro.tf.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.
cell_state_init (callable): Initializer function for the
initial cell state. The functino should return a tf.Tensor.
cell_state_init_trainable (bool): Bool for whether the initial
cell state is trainable.
state_include_action (bool): Whether the state includes action.
If True, input dimension will be
(observation dimension + action dimension).
forget_bias (bool): If True, add 1 to the bias of the forget gate
at initialization. It's used to reduce the scale of forgetting at
the beginning of the training.
layer_normalization (bool): Bool for using layer normalization or not.
"""
def __init__(self,
env_spec,
name='CategoricalLSTMPolicyWithModel',
hidden_dim=32,
hidden_nonlinearity=tf.nn.tanh,
hidden_w_init=tf.glorot_uniform_initializer(),
hidden_b_init=tf.zeros_initializer(),
recurrent_nonlinearity=tf.nn.sigmoid,
recurrent_w_init=tf.glorot_uniform_initializer(),
output_nonlinearity=tf.nn.softmax,
output_w_init=tf.glorot_uniform_initializer(),
output_b_init=tf.zeros_initializer(),
hidden_state_init=tf.zeros_initializer(),
hidden_state_init_trainable=False,
cell_state_init=tf.zeros_initializer(),
cell_state_init_trainable=False,
state_include_action=True,
forget_bias=True,
layer_normalization=False):
if not isinstance(env_spec.action_space, Discrete):
raise ValueError('CategoricalLSTMPolicy only works'
'with akro.tf.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._state_include_action = state_include_action
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._cell_state_init = cell_state_init
if state_include_action:
self._input_dim = self._obs_dim + self._action_dim
else:
self._input_dim = self._obs_dim
self.model = LSTMModel(
output_dim=self._action_dim,
hidden_dim=self._hidden_dim,
name='prob_network',
forget_bias=forget_bias,
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,
cell_state_init=cell_state_init,
cell_state_init_trainable=cell_state_init_trainable,
output_nonlinearity=output_nonlinearity,
output_w_init=output_w_init,
output_b_init=output_b_init,
layer_normalization=layer_normalization)
self._initialize()
def _initialize(self):
obs_ph = tf.placeholder(tf.float32,
shape=(None, None, self._input_dim))
step_input_var = tf.placeholder(shape=(None, self._input_dim),
name='step_input',
dtype=tf.float32)
step_hidden_var = tf.placeholder(shape=(None, self._hidden_dim),
name='step_hidden_input',
dtype=tf.float32)
step_cell_var = tf.placeholder(shape=(None, self._hidden_dim),
name='step_cell_input',
dtype=tf.float32)
with tf.variable_scope(self.name) as vs:
self._variable_scope = vs
self.model.build(obs_ph, step_input_var, step_hidden_var,
step_cell_var)
self._f_step_prob = tf.get_default_session().make_callable(
[
self.model.networks['default'].step_output,
self.model.networks['default'].step_hidden,
self.model.networks['default'].step_cell
],
feed_list=[step_input_var, step_hidden_var, step_cell_var])
self.prev_actions = None
self.prev_hiddens = None
self.prev_cells = None
@property
def vectorized(self):
"""Vectorized or not."""
return True
def dist_info_sym(self, obs_var, state_info_vars, name=None):
"""Symbolic graph of the distribution."""
if self._state_include_action:
prev_action_var = state_info_vars['prev_action']
prev_action_var = tf.cast(prev_action_var, tf.float32)
all_input_var = tf.concat(axis=2,
values=[obs_var, prev_action_var])
else:
all_input_var = obs_var
with tf.variable_scope(self._variable_scope):
outputs, _, _, _, _, _ = self.model.build(
all_input_var,
self.model.networks['default'].step_input,
self.model.networks['default'].step_hidden_input,
self.model.networks['default'].step_cell_input,
name=name)
return dict(prob=outputs)
def reset(self, dones=None):
"""Reset the policy."""
if dones is None:
dones = [True]
dones = np.asarray(dones)
if self.prev_actions is None or len(dones) != len(self.prev_actions):
self.prev_actions = np.zeros(
(len(dones), self.action_space.flat_dim))
self.prev_hiddens = np.zeros((len(dones), self._hidden_dim))
self.prev_cells = np.zeros((len(dones), self._hidden_dim))
self.prev_actions[dones] = 0.
self.prev_hiddens[dones] = self.model.networks[
'default'].init_hidden.eval()
self.prev_cells[dones] = self.model.networks['default'].init_cell.eval(
)
def get_action(self, observation):
"""Return a single action."""
actions, agent_infos = self.get_actions([observation])
return actions[0], {k: v[0] for k, v in agent_infos.items()}
def get_actions(self, observations):
"""Return multiple actions."""
flat_obs = self.observation_space.flatten_n(observations)
if self._state_include_action:
assert self.prev_actions is not None
all_input = np.concatenate([flat_obs, self.prev_actions], axis=-1)
else:
all_input = flat_obs
probs, hidden_vec, cell_vec = self._f_step_prob(
all_input, self.prev_hiddens, self.prev_cells)
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
self.prev_cells = cell_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 recurrent(self):
"""Recurrent or not."""
return True
@property
def distribution(self):
"""Policy distribution."""
return RecurrentCategorical(self._action_dim)
@property
def state_info_specs(self):
"""State info specification."""
if self._state_include_action:
return [
('prev_action', (self._action_dim, )),
]
else:
return []
def __getstate__(self):
"""Object.__getstate__."""
new_dict = super().__getstate__()
del new_dict['_f_step_prob']
return new_dict
def __setstate__(self, state):
"""Object.__setstate__."""
super().__setstate__(state)
self._initialize()
def test_is_pickleable(self):
model = LSTMModel(output_dim=1, hidden_dim=1)
step_hidden_var = tf.placeholder(
shape=(self.batch_size, 1), name='step_hidden', dtype=tf.float32)
step_cell_var = tf.placeholder(
shape=(self.batch_size, 1), name='step_cell', dtype=tf.float32)
model.build(self._input_var, self._step_input_var, step_hidden_var,
step_cell_var)
# assign bias to all one
with tf.variable_scope('LSTMModel/lstm', reuse=True):
init_hidden = tf.get_variable('initial_hidden')
init_hidden.load(tf.ones_like(init_hidden).eval())
hidden = np.zeros((self.batch_size, 1))
cell = np.zeros((self.batch_size, 1))
outputs1 = self.sess.run(
model.networks['default'].all_output,
feed_dict={self._input_var: self.obs_inputs})
output1 = self.sess.run(
[
model.networks['default'].step_output,
model.networks['default'].step_hidden,
model.networks['default'].step_cell
],
feed_dict={
self._step_input_var: self.obs_input,
step_hidden_var: hidden,
step_cell_var: cell
})
h = pickle.dumps(model)
with tf.Session(graph=tf.Graph()) as sess:
model_pickled = pickle.loads(h)
input_var = tf.placeholder(
tf.float32,
shape=(None, None, self.feature_shape),
name='input')
step_input_var = tf.placeholder(
tf.float32, shape=(None, self.feature_shape), name='input')
step_hidden_var = tf.placeholder(
shape=(self.batch_size, 1),
name='initial_hidden',
dtype=tf.float32)
step_cell_var = tf.placeholder(
shape=(self.batch_size, 1),
name='initial_cell',
dtype=tf.float32)
model_pickled.build(input_var, step_input_var, step_hidden_var,
step_cell_var)
outputs2 = sess.run(
model_pickled.networks['default'].all_output,
feed_dict={input_var: self.obs_inputs})
output2 = sess.run(
[
model_pickled.networks['default'].step_output,
model_pickled.networks['default'].step_hidden,
model_pickled.networks['default'].step_cell
],
feed_dict={
step_input_var: self.obs_input,
step_hidden_var: hidden,
step_cell_var: cell
})
assert np.array_equal(outputs1, outputs2)
assert np.array_equal(output1, output2)
