class CategoricalLSTMPolicy2(StochasticPolicy2):
"""Categorical LSTM Policy.
A policy represented by a Categorical distribution
which is parameterized by a Long short-term memory (LSTM).
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._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_state_init_trainable = cell_state_init_trainable
self._forget_bias = forget_bias
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 = CategoricalLSTMModel(
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
def build(self, state_input, name=None):
"""Build model.
Args:
state_input (tf.Tensor) : State input.
name (str): Name of the model, which is also the name scope.
"""
with tf.compat.v1.variable_scope(self.name) as vs:
self._variable_scope = vs
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)
self.model.build(state_input,
step_input_var,
step_hidden_var,
step_cell_var,
name=name)
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 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_cells = np.zeros((len(do_resets), self._hidden_dim))
self._prev_actions[do_resets] = 0.
self._prev_hiddens[do_resets] = self.model.networks[
'default'].init_hidden.eval()
self._prev_cells[do_resets] = self.model.networks[
'default'].init_cell.eval()
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()}
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, 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: Whether policy is recurrent or not.
"""
return True
@property
def distribution(self):
"""Policy distribution.
Returns:
tfp.Distribution.OneHotCategorical: Policy distribution.
"""
return self.model.networks['default'].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 []
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.CategoricalLSTMPolicy2: 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,
cell_state_init=self._cell_state_init,
cell_state_init_trainable=self._cell_state_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 test_is_pickleable(self):
model = CategoricalLSTMModel(output_dim=1, hidden_dim=1)
step_hidden_var = tf.compat.v1.placeholder(shape=(self.batch_size, 1),
name='step_hidden',
dtype=tf.float32)
step_cell_var = tf.compat.v1.placeholder(shape=(self.batch_size, 1),
name='step_cell',
dtype=tf.float32)
network = model.build(self._input_var, self._step_input_var,
step_hidden_var, step_cell_var)
dist = network.dist
# assign bias to all one
with tf.compat.v1.variable_scope('CategoricalLSTMModel/lstm',
reuse=True):
init_hidden = tf.compat.v1.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(dist.probs,
feed_dict={self._input_var: self.obs_inputs})
output1 = self.sess.run(
[network.step_output, network.step_hidden, network.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.compat.v1.Session(graph=tf.Graph()) as sess:
model_pickled = pickle.loads(h)
input_var = tf.compat.v1.placeholder(tf.float32,
shape=(None, None,
self.feature_shape),
name='input')
step_input_var = tf.compat.v1.placeholder(
tf.float32, shape=(None, self.feature_shape), name='input')
step_hidden_var = tf.compat.v1.placeholder(shape=(self.batch_size,
1),
name='initial_hidden',
dtype=tf.float32)
step_cell_var = tf.compat.v1.placeholder(shape=(self.batch_size,
1),
name='initial_cell',
dtype=tf.float32)
network2 = model_pickled.build(input_var, step_input_var,
step_hidden_var, step_cell_var)
dist2 = network2.dist
outputs2 = sess.run(dist2.probs,
feed_dict={input_var: self.obs_inputs})
output2 = sess.run(
[
network2.step_output, network2.step_hidden,
network2.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)