def __call__(self, inputs):
inputs = tf_utils.batch_concat(inputs)
zero = tf.constant(0, dtype=inputs.dtype)
mean = self._mean_layer(inputs)
if self._binary_grip_action:
grip_pred = tf.gather(
tf.constant([2.0, -2.0]), tf.math.argmax(mean[:, :2], axis=1))
mean = tf.concat([tf.expand_dims(grip_pred, axis=1), mean[:, 2:]], axis=1)
if self._fixed_scale:
scale = tf.ones_like(mean) * self._init_scale
else:
scale = tf.nn.softplus(self._scale_layer(inputs))
scale *= self._init_scale / tf.nn.softplus(zero)
scale += self._min_scale
# Maybe transform the mean.
if self._tanh_mean:
mean = tf.tanh(mean)
if self._use_tfd_independent:
dist = tfd.Independent(tfd.Normal(loc=mean, scale=scale))
else:
dist = tfd.MultivariateNormalDiag(loc=mean, scale_diag=scale)
return dist
def test_batch_concat(self):
batch_size = 32
inputs = [
tf.zeros(shape=(batch_size, 2)),
{
'foo': tf.zeros(shape=(batch_size, 5, 3))
},
[tf.zeros(shape=(batch_size, 1))],
]
output_shape = tf2_utils.batch_concat(inputs).shape.as_list()
expected_shape = [batch_size, 2 + 5 * 3 + 1]
self.assertSequenceEqual(output_shape, expected_shape)
def _density_loss(self, current_obs, action, discount, next_obs):
target = tf2_utils.batch_concat(next_obs)
# density = self.mixture_density(current_obs, action)
obs_distr, discount_distr = self.mixture_density(current_obs, action)
obs_log_prob = obs_distr.log_prob(target)
obs_loss = tf.reduce_mean(-obs_log_prob)
discount_log_prob = discount_distr.log_prob(discount)
discount_loss = tf.reduce_mean(-discount_log_prob)
loss = obs_loss + discount_loss
return loss, obs_loss, discount_loss
def batch_concat_selection(observation_dict: Dict[str, types.NestedTensor],
concat_keys: Optional[Iterable[str]] = None,
output_dtype=tf.float32) -> tf.Tensor:
"""Concatenate a dict of observations into 2-D tensors."""
concat_keys = concat_keys or sorted(observation_dict.keys())
to_concat = []
for obs in concat_keys:
if obs not in observation_dict:
raise KeyError(
'Missing observation. Requested: {} (available: {})'.format(
obs, list(observation_dict.keys())))
to_concat.append(tf.cast(observation_dict[obs], output_dtype))
return tf2_utils.batch_concat(to_concat)
def __call__(self, observation: tf.Tensor, action: tf.Tensor) -> tf.Tensor:
# Maybe transform observations and actions before feeding them on.
if self._observation_network:
observation = self._observation_network(observation)
if self._action_network:
action = self._action_network(action)
# Concat observations and actions, with one batch dimension.
outputs = tf2_utils.batch_concat([observation, action])
# Maybe transform output before returning.
if self._critic_network:
outputs = self._critic_network(outputs)
return outputs
def __call__(
self,
x: snt.Module,
state: Optional[snt.Module] = None,
message: Optional[snt.Module] = None,
) -> snt.Module:
if state is None:
state = self.initial_state(x.shape[0])
if message is None:
message = self.initial_message(x.shape[0])
obs_in = self._obs_in_network(x)
comm_in = self._comm_in_network(message)
core_in = tf2_utils.batch_concat([obs_in, comm_in])
core_out, state = self._core_network(core_in, state)
action = self._action_head(core_out)
message = self._comm_head(core_out)
return (action, message), state
def __call__(self, observation: types.NestedTensor,
action: types.NestedTensor) -> tf.Tensor:
# Maybe transform observations and actions before feeding them on.
if self._observation_network:
observation = self._observation_network(observation)
if self._action_network:
action = self._action_network(action)
if hasattr(observation, 'dtype') and hasattr(action, 'dtype'):
if observation.dtype != action.dtype:
# Observation and action must be the same type for concat to work
action = tf.cast(action, observation.dtype)
# Concat observations and actions, with one batch dimension.
outputs = tf2_utils.batch_concat([observation, action])
# Maybe transform output before returning.
if self._critic_network:
outputs = self._critic_network(outputs)
return outputs
def __call__(self, inputs, action: tf.Tensor = None, task=None):
"""Evaluates the ControlNetwork.
Args:
inputs: A dictionary of agent observation tensors.
action: Agent actions.
task: Optional encoding of the task.
Raises:
ValueError: if neither proprio_input is provided.
ValueError: if some proprio input looks suspiciously like pixel inputs.
Returns:
Processed network output.
"""
if not isinstance(inputs, dict):
inputs = {'inputs': inputs}
proprio_input = []
# By default, treat all observations as proprioceptive.
if self._proprio_keys is None:
self._proprio_keys = list(sorted(inputs.keys()))
for key in self._proprio_keys:
proprio_input.append(snt.Flatten()(inputs[key]))
if np.prod(inputs[key].shape[1:]) > 32 * 32 * 3:
raise ValueError(
'This input does not resemble a proprioceptive '
'state: {} with shape {}'.format(key, inputs[key].shape))
# Append optional action input (i.e. for critic networks).
if action is not None:
proprio_input.append(action)
proprio_input = tf2_utils.batch_concat(proprio_input)
proprio_state = self._proprio_encoder(proprio_input)
return proprio_state
def __call__(self, observations: types.Nest) -> tf.Tensor:
"""Forwards the policy network."""
return self._network(tf2_utils.batch_concat(observations))
