def observe(self, exp):
# The shape is [learn_queue_cap, unroll_length, env_batch_size, ...]
exp = tf.nest.map_structure(lambda e: common.transpose2(e, 1, 2), exp)
# flatten the shape (num_envs, env_batch_size)
self._experience = tf.nest.map_structure(flatten_once, exp)
if self._batch_size is None:
self._batch_size = self._experience.step_type.shape[0]
def train_step(self,
time_step: ActionTimeStep,
state,
calc_intrinsic_reward=True):
"""
Args:
time_step (ActionTimeStep): input time_step data
state (tuple): state for MISC (previous observation,
previous previous action)
calc_intrinsic_reward (bool): if False, only return the losses
Returns:
TrainStep:
outputs: empty tuple ()
state: tuple of observation and previous action
info: (MISCInfo):
"""
feature = time_step.observation
prev_action = time_step.prev_action
feature = tf.concat([feature_state, prev_action], axis=-1)
prev_feature = tf.concat(state, axis=-1)
feature_reshaped = tf.expand_dims(feature, axis=1)
prev_feature_reshaped = tf.expand_dims(prev_feature, axis=1)
feature_pair = tf.concat([prev_feature_reshaped, feature_reshaped], 1)
feature_reshaped_tran = transpose2(feature_reshaped, 1, 0)
def add_batch():
self._buffer.add_batch(feature_reshaped_tran)
if calc_intrinsic_reward:
add_batch()
if self._n_objects < 2:
obs_tau_excludes_goal, obs_tau_achieved_goal = \
self._split_observation_fn(feature_pair)
loss = self._mine(obs_tau_excludes_goal, obs_tau_achieved_goal)
elif self._n_objects == 2:
obs_tau_excludes_goal, obs_tau_achieved_goal_1, obs_tau_achieved_goal_2 \
= self._split_observation_fn(
feature_pair)
loss_1 = self._mine(obs_tau_excludes_goal, obs_tau_achieved_goal_1)
loss_2 = self._mine(obs_tau_excludes_goal, obs_tau_achieved_goal_2)
loss = loss_1 + loss_2
intrinsic_reward = ()
if calc_intrinsic_reward:
# scale/normalize the MISC intrinsic reward
if self._n_objects < 2:
intrinsic_reward = tf.clip_by_value(self._mi_r_scale * loss, 0,
1)
elif self._n_objects == 2:
intrinsic_reward = tf.clip_by_value(
self._mi_r_scale * loss_1, 0,
1) + 1 * tf.clip_by_value(self._mi_r_scale * loss_2, 0, 1)
return AlgorithmStep(
outputs=(), state=[feature_state, prev_action], \
info=MISCInfo(reward=intrinsic_reward))
def calc_loss(self, info: MISCInfo):
feature_tau_sampled = self._buffer.get_batch(
batch_size=self._buffer_size)
feature_tau_sampled_tran = transpose2(feature_tau_sampled, 1, 0)
if self._n_objects < 2:
obs_tau_excludes_goal, obs_tau_achieved_goal = (
self._split_observation_fn(feature_tau_sampled_tran))
loss = self._mine(obs_tau_excludes_goal, obs_tau_achieved_goal)
elif self._n_objects == 2:
(obs_tau_excludes_goal, obs_tau_achieved_goal_1,
obs_tau_achieved_goal_2
) = self._split_observation_fn(feature_tau_sampled_tran)
loss_1 = self._mine(obs_tau_excludes_goal, obs_tau_achieved_goal_1)
loss_2 = self._mine(obs_tau_excludes_goal, obs_tau_achieved_goal_2)
loss = loss_1 + loss_2
neg_loss = -loss
neg_loss_scalar = tf.reduce_mean(neg_loss)
return LossInfo(scalar_loss=neg_loss_scalar)
def get_training_exps(self):
"""
Get training experiences from the learning queue
Returns:
exp (Experience):
env_id (tf.tensor): if not None, has the shape of (`num_envs`). Each
element of `env_ids` indicates which batched env the data come from.
steps (int): how many environment steps this batch of exps contain
"""
batch = make_learning_batch(*self._tfq.learn_queue.dequeue_all())
# convert the batch to the experience format
exp = make_experience(batch.time_step, batch.policy_step,
batch.act_dist_param)
# make the exp batch major for each environment
exp = tf.nest.map_structure(lambda e: common.transpose2(e, 1, 2), exp)
num_envs, unroll_length, env_batch_size \
= batch.time_step.observation.shape[:3]
steps = num_envs * unroll_length * env_batch_size
return exp, batch.env_id, steps
def _make_time_major(nest):
"""Put the time dim to axis=0."""
return tf.nest.map_structure(lambda x: common.transpose2(x, 0, 1),
nest)