def test_is_pickleable(self, output_dim, hidden_sizes):
model = CategoricalMLPModel(output_dim=output_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=None,
hidden_w_init=tf.ones_initializer(),
output_w_init=tf.ones_initializer())
dist = model.build(self.input_var)
# assign bias to all one
with tf.compat.v1.variable_scope('CategoricalMLPModel/mlp',
reuse=True):
bias = tf.compat.v1.get_variable('hidden_0/bias')
bias.load(tf.ones_like(bias).eval())
output1 = self.sess.run(dist.logits,
feed_dict={self.input_var: self.obs})
h = pickle.dumps(model)
with tf.compat.v1.Session(graph=tf.Graph()) as sess:
input_var = tf.compat.v1.placeholder(tf.float32, shape=(None, 5))
model_pickled = pickle.loads(h)
dist2 = model_pickled.build(input_var)
output2 = sess.run(dist2.logits, feed_dict={input_var: self.obs})
assert np.array_equal(output1, output2)
def test_output_nonlinearity(self):
model = CategoricalMLPModel(output_dim=1,
output_nonlinearity=lambda x: x / 2)
obs_ph = tf.compat.v1.placeholder(tf.float32, shape=(None, 1))
obs = np.ones((1, 1))
dist = model.build(obs_ph)
probs = tf.compat.v1.get_default_session().run(dist.probs,
feed_dict={obs_ph: obs})
assert probs == [0.5]
def test_output_normalized(self, output_dim):
model = CategoricalMLPModel(output_dim=output_dim)
obs_ph = tf.compat.v1.placeholder(tf.float32, shape=(None, output_dim))
obs = np.ones((1, output_dim))
dist = model.build(obs_ph)
probs = tf.compat.v1.get_default_session().run(tf.reduce_sum(
dist.probs),
feed_dict={obs_ph: obs})
assert np.isclose(probs, 1.0)
def __init__(self,
env_spec,
name='CategoricalMLPPolicy',
hidden_sizes=(32, 32),
hidden_nonlinearity=tf.nn.tanh,
hidden_w_init=tf.initializers.glorot_uniform(),
hidden_b_init=tf.zeros_initializer(),
output_nonlinearity=None,
output_w_init=tf.initializers.glorot_uniform(),
output_b_init=tf.zeros_initializer(),
layer_normalization=False):
if not isinstance(env_spec.action_space, akro.Discrete):
raise ValueError('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._hidden_sizes = hidden_sizes
self._hidden_nonlinearity = hidden_nonlinearity
self._hidden_w_init = hidden_w_init
self._hidden_b_init = hidden_b_init
self._output_nonlinearity = output_nonlinearity
self._output_w_init = output_w_init
self._output_b_init = output_b_init
self._layer_normalization = layer_normalization
self._f_prob = None
self._dist = None
self.model = CategoricalMLPModel(
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='CategoricalMLPModel')
self._initialize()
def test_dist(self):
model = CategoricalMLPModel(output_dim=1)
dist = model.build(self.input_var)
assert isinstance(dist, tfp.distributions.OneHotCategorical)
class CategoricalMLPPolicy2(StochasticPolicy2):
"""Categorical MLP Policy.
A policy represented by a Categorical distribution
which is parameterized by a multilayer perceptron (MLP).
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.initializers.glorot_uniform(),
hidden_b_init=tf.zeros_initializer(),
output_nonlinearity=None,
output_w_init=tf.initializers.glorot_uniform(),
output_b_init=tf.zeros_initializer(),
layer_normalization=False):
if not isinstance(env_spec.action_space, akro.Discrete):
raise ValueError('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._hidden_sizes = hidden_sizes
self._hidden_nonlinearity = hidden_nonlinearity
self._hidden_w_init = hidden_w_init
self._hidden_b_init = hidden_b_init
self._output_nonlinearity = output_nonlinearity
self._output_w_init = output_w_init
self._output_b_init = output_b_init
self._layer_normalization = layer_normalization
self._f_prob = None
self._dist = None
self.model = CategoricalMLPModel(
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='CategoricalMLPModel')
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
self._dist = self.model.build(state_input, name=name)
self._f_prob = tf.compat.v1.get_default_session().make_callable(
[tf.argmax(self._dist.sample(), -1), self._dist.probs],
feed_list=[state_input])
@property
def distribution(self):
"""Policy distribution.
Returns:
tfp.Distribution.OneHotCategorical: Policy distribution.
"""
return self._dist
@property
def vectorized(self):
"""Vectorized or not.
Returns:
Bool: True if primitive supports vectorized operations.
"""
return True
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.
"""
sample, prob = self._f_prob([observation])
return sample[0], dict(prob=prob[0])
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.
"""
samples, probs = self._f_prob(observations)
return samples, dict(prob=probs)
def get_regularizable_vars(self):
"""Get regularizable weight variables under the Policy scope.
Returns:
list[tf.Tensor]: Trainable variables.
"""
trainable = self.get_trainable_vars()
return [
var for var in trainable
if 'hidden' in var.name and 'kernel' in var.name
]
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.Policy: Newly cloned policy.
"""
return self.__class__(name=name,
env_spec=self._env_spec,
hidden_sizes=self._hidden_sizes,
hidden_nonlinearity=self._hidden_nonlinearity,
hidden_w_init=self._hidden_w_init,
hidden_b_init=self._hidden_b_init,
output_nonlinearity=self._output_nonlinearity,
output_w_init=self._output_w_init,
output_b_init=self._output_b_init,
layer_normalization=self._layer_normalization)
def __getstate__(self):
"""Object.__getstate__.
Returns:
dict: State dictionary.
"""
new_dict = super().__getstate__()
del new_dict['_f_prob']
del new_dict['_dist']
return new_dict