def train_step(self, exp: Experience, state):
new_state = AgentState()
info = AgentInfo()
skill_generator_info = exp.rollout_info.skill_generator
subtrajectory = self._skill_generator.update_disc_subtrajectory(
exp, state.skill_generator)
skill_step = self._skill_generator.train_step(
exp._replace(rollout_info=skill_generator_info),
state.skill_generator)
info = info._replace(skill_generator=skill_step.info)
new_state = new_state._replace(skill_generator=skill_step.state)
exp = transform_nest(exp, "observation", self._observation_transformer)
observation = self._make_low_level_observation(
subtrajectory, skill_step.output,
skill_generator_info.switch_skill, skill_generator_info.steps,
skill_step.state.discriminator.first_observation)
rl_step = self._rl_algorithm.train_step(
exp._replace(observation=observation,
rollout_info=exp.rollout_info.rl), state.rl)
new_state = new_state._replace(rl=rl_step.state)
info = info._replace(rl=rl_step.info)
return AlgStep(output=rl_step.output, state=new_state, info=info)
def preprocess_experience(self, exp: Experience):
ac_info = exp.rollout_info.ac._replace(
action_distribution=exp.rollout_info.action_distribution)
new_exp = self._ac_algorithm.preprocess_experience(
exp._replace(rollout_info=ac_info))
return new_exp._replace(rollout_info=exp.rollout_info._replace(
ac=new_exp.rollout_info))
def preprocess_experience(self, experience: Experience):
"""Fill experience.rollout_info with PredictiveRepresentationLearnerInfo
Note that the shape of experience is [B, T, ...].
The target is a Tensor (or a nest of Tensors) when there is only one
decoder. When there are multiple decorders, the target is a list,
and each of its element is a Tensor (or a nest of Tensors), which is
used as the target for the corresponding decoder.
"""
assert experience.batch_info != ()
batch_info: BatchInfo = experience.batch_info
replay_buffer: ReplayBuffer = experience.replay_buffer
mini_batch_length = experience.step_type.shape[1]
with alf.device(replay_buffer.device):
# [B, 1]
positions = convert_device(batch_info.positions).unsqueeze(-1)
# [B, 1]
env_ids = convert_device(batch_info.env_ids).unsqueeze(-1)
# [B, T]
positions = positions + torch.arange(mini_batch_length)
# [B, T]
steps_to_episode_end = replay_buffer.steps_to_episode_end(
positions, env_ids)
# [B, T]
episode_end_positions = positions + steps_to_episode_end
# [B, T, unroll_steps+1]
positions = positions.unsqueeze(-1) + torch.arange(
self._num_unroll_steps + 1)
# [B, 1, 1]
env_ids = env_ids.unsqueeze(-1)
# [B, T, 1]
episode_end_positions = episode_end_positions.unsqueeze(-1)
# [B, T, unroll_steps+1]
mask = positions <= episode_end_positions
# [B, T, unroll_steps+1]
positions = torch.min(positions, episode_end_positions)
# [B, T, unroll_steps+1, ...]
target = replay_buffer.get_field(self._target_fields, env_ids,
positions)
# [B, T, unroll_steps+1]
action = replay_buffer.get_field('action', env_ids, positions)
rollout_info = PredictiveRepresentationLearnerInfo(action=action,
mask=mask,
target=target)
rollout_info = convert_device(rollout_info)
return experience._replace(rollout_info=rollout_info)
def train_step(self, exp: Experience, state):
new_state = AgentState()
info = AgentInfo()
observation = exp.observation
if self._representation_learner is not None:
repr_step = self._representation_learner.train_step(
exp._replace(rollout_info=exp.rollout_info.repr), state.repr)
new_state = new_state._replace(repr=repr_step.state)
info = info._replace(repr=repr_step.info)
observation = repr_step.output
if self._goal_generator is not None:
goal_step = self._goal_generator.train_step(
exp._replace(
observation=observation,
rollout_info=exp.rollout_info.goal_generator),
state.goal_generator)
info = info._replace(goal_generator=goal_step.info)
new_state = new_state._replace(goal_generator=goal_step.state)
observation = [observation, goal_step.output]
if self._irm is not None:
irm_step = self._irm.train_step(
exp._replace(observation=observation), state=state.irm)
info = info._replace(irm=irm_step.info)
new_state = new_state._replace(irm=irm_step.state)
rl_step = self._rl_algorithm.train_step(
exp._replace(
observation=observation, rollout_info=exp.rollout_info.rl),
state.rl)
new_state = new_state._replace(rl=rl_step.state)
info = info._replace(rl=rl_step.info)
if self._entropy_target_algorithm:
assert 'action_distribution' in rl_step.info._fields, (
"PolicyStep from rl_algorithm.train_step() does not contain "
"`action_distribution`, which is required by "
"`enforce_entropy_target`")
et_step = self._entropy_target_algorithm.train_step(
rl_step.info.action_distribution, step_type=exp.step_type)
info = info._replace(entropy_target=et_step.info)
return AlgStep(output=rl_step.output, state=new_state, info=info)
def preprocess_experience(self, exp: Experience):
self.summarize_reward("training_reward/extrinsic", exp.reward)
# relabel exp with intrinsic rewards by goal generator
skill_rollout_info = exp.rollout_info.skill_generator
new_exp = self._rl_algorithm.preprocess_experience(
exp._replace(reward=skill_rollout_info.reward,
discount=exp.discount *
exp.rollout_info.skill_discount,
rollout_info=exp.rollout_info.rl))
return new_exp._replace(rollout_info=exp.rollout_info._replace(
rl=new_exp.rollout_info))
def preprocess_experience(self, experience: Experience):
"""Fill experience.rollout_info with PredictiveRepresentationLearnerInfo
Note that the shape of experience is [B, T, ...]
"""
assert experience.batch_info != ()
batch_info: BatchInfo = experience.batch_info
replay_buffer: ReplayBuffer = experience.replay_buffer
mini_batch_length = experience.step_type.shape[1]
with alf.device(replay_buffer.device):
# [B, 1]
positions = convert_device(batch_info.positions).unsqueeze(-1)
# [B, 1]
env_ids = convert_device(batch_info.env_ids).unsqueeze(-1)
# [B, T]
positions = positions + torch.arange(mini_batch_length)
# [B, T]
steps_to_episode_end = replay_buffer.steps_to_episode_end(
positions, env_ids)
# [B, T]
episode_end_positions = positions + steps_to_episode_end
# [B, T, unroll_steps+1]
positions = positions.unsqueeze(-1) + torch.arange(
self._num_unroll_steps + 1)
# [B, 1, 1]
env_ids = env_ids.unsqueeze(-1)
# [B, T, 1]
episode_end_positions = episode_end_positions.unsqueeze(-1)
# [B, T, unroll_steps+1]
mask = positions <= episode_end_positions
# [B, T, unroll_steps+1]
positions = torch.min(positions, episode_end_positions)
# [B, T, unroll_steps+1]
target = replay_buffer.get_field(self._target_fields, env_ids,
positions)
# [B, T, unroll_steps+1]
action = replay_buffer.get_field('action', env_ids, positions)
rollout_info = PredictiveRepresentationLearnerInfo(action=action,
mask=mask,
target=target)
rollout_info = convert_device(rollout_info)
return experience._replace(rollout_info=rollout_info)
def preprocess_experience(self, exp: Experience):
"""Add intrinsic rewards to extrinsic rewards if there is an intrinsic
reward module. Also call ``preprocess_experience()`` of the rl
algorithm.
"""
reward = self.calc_training_reward(exp.reward, exp.rollout_info)
exp = exp._replace(reward=reward)
if self._representation_learner:
new_exp = self._representation_learner.preprocess_experience(
exp._replace(rollout_info=exp.rollout_info.repr,
rollout_info_field=exp.rollout_info_field +
'.repr'))
exp = new_exp._replace(rollout_info=exp.rollout_info._replace(
repr=new_exp.rollout_info))
new_exp = self._rl_algorithm.preprocess_experience(
exp._replace(rollout_info=exp.rollout_info.rl,
rollout_info_field=exp.rollout_info_field + '.rl'))
return new_exp._replace(
rollout_info=exp.rollout_info._replace(rl=new_exp.rollout_info),
rollout_info_field=exp.rollout_info_field)
def observe(self, exp: Experience):
"""An algorithm can override to manipulate experience.
Args:
exp (Experience): The shapes can be either [Q, T, B, ...] or
[B, ...], where Q is `learn_queue_cap` in `AsyncOffPolicyDriver`,
T is the sequence length, and B is the batch size of the batched
environment.
"""
if not self._use_rollout_state:
exp = exp._replace(state=())
exp = nest_utils.distributions_to_params(exp)
for observer in self._exp_observers:
observer(exp)
def preprocess_experience(self, exp: Experience):
"""Compute advantages and put it into exp.rollout_info."""
advantages = value_ops.generalized_advantage_estimation(
rewards=exp.reward,
values=exp.rollout_info.value,
step_types=exp.step_type,
discounts=exp.discount * self._loss._gamma,
td_lambda=self._loss._lambda,
time_major=False)
advantages = torch.cat([
advantages,
torch.zeros(*advantages.shape[:-1], 1, dtype=advantages.dtype)
],
dim=-1)
returns = exp.rollout_info.value + advantages
return exp._replace(rollout_info=PPOInfo(
exp.rollout_info.action_distribution, returns, advantages))
def train_step(self, rl_exp: Experience, state: ActionRepeatState):
"""Train the underlying RL algorithm ``self._rl``. Because in
``self.rollout_step()`` the replay buffer only stores info related to
``self._rl``, here we can directly call ``self._rl.train_step()``.
Args:
rl_exp (Experience): experiences that have been transformed to be
learned by ``self._rl``.
state (ActionRepeatState):
"""
repr_state = ()
if self._repr_learner is not None:
repr_step = self._repr_learner.train_step(rl_exp, state.repr)
rl_exp = rl_exp._replace(observation=repr_step.output)
repr_state = repr_step.state
rl_step = self._rl.train_step(rl_exp, state.rl)
new_state = ActionRepeatState(rl=rl_step.state, repr=repr_state)
return rl_step._replace(state=new_state)
def train_step(self, exp: Experience, state):
ac_info = exp.rollout_info.ac._replace(
action_distribution=exp.rollout_info.action_distribution)
exp = exp._replace(rollout_info=ac_info)
policy_step = self._ac_algorithm.train_step(exp, state)
return self._make_policy_step(exp, state, policy_step)
def transform_experience(self, experience: Experience):
if self._stack_size == 1:
return experience
assert experience.batch_info != ()
batch_info: BatchInfo = experience.batch_info
replay_buffer: ReplayBuffer = experience.replay_buffer
with alf.device(replay_buffer.device):
# [B]
env_ids = convert_device(batch_info.env_ids)
# [B]
positions = convert_device(batch_info.positions)
prev_positions = torch.arange(self._stack_size -
1) - self._stack_size + 1
# [B, stack_size - 1]
prev_positions = positions.unsqueeze(
-1) + prev_positions.unsqueeze(0)
episode_begin_positions = replay_buffer.get_episode_begin_position(
positions, env_ids)
# [B, 1]
episode_begin_positions = episode_begin_positions.unsqueeze(-1)
# [B, stack_size - 1]
prev_positions = torch.max(prev_positions, episode_begin_positions)
# [B, 1]
env_ids = env_ids.unsqueeze(-1)
assert torch.all(
prev_positions[:, 0] >= replay_buffer.get_earliest_position(
env_ids)
), ("Some previous posisions are no longer in the replay buffer")
batch_size, mini_batch_length = experience.step_type.shape
# [[0, 1, ..., stack_size-1],
# [1, 2, ..., stack_size],
# ...
# [mini_batch_length - 1, ...]]
#
# [mini_batch_length, stack_size]
obs_index = (torch.arange(self._stack_size).unsqueeze(0) +
torch.arange(mini_batch_length).unsqueeze(1))
B = torch.arange(batch_size)
obs_index = (B.unsqueeze(-1).unsqueeze(-1), obs_index.unsqueeze(0))
def _stack_frame(obs, i):
prev_obs = replay_buffer.get_field(self._exp_fields[i], env_ids,
prev_positions)
prev_obs = convert_device(prev_obs)
stacked_shape = alf.nest.get_field(
self._transformed_observation_spec, self._fields[i]).shape
# [batch_size, mini_batch_length + stack_size - 1, ...]
stacked_obs = torch.cat((prev_obs, obs), dim=1)
# [batch_size, mini_batch_length, stack_size, ...]
stacked_obs = stacked_obs[obs_index]
if self._stack_axis != 0 and obs.ndim > 3:
stack_axis = self._stack_axis
if stack_axis < 0:
stack_axis += stacked_obs.ndim
else:
stack_axis += 3
stacked_obs = stacked_obs.unsqueeze(stack_axis)
stacked_obs = stacked_obs.transpose(2, stack_axis)
stacked_obs = stacked_obs.squeeze(2)
stacked_obs = stacked_obs.reshape(batch_size, mini_batch_length,
*stacked_shape)
return stacked_obs
observation = experience.observation
for i, field in enumerate(self._fields):
observation = alf.nest.transform_nest(observation, field,
partial(_stack_frame, i=i))
return experience._replace(observation=observation)
def preprocess_experience(self, experience: Experience):
"""Fill experience.rollout_info with MuzeroInfo
Note that the shape of experience is [B, T, ...]
"""
assert experience.batch_info != ()
batch_info: BatchInfo = experience.batch_info
replay_buffer: ReplayBuffer = experience.replay_buffer
info_path: str = experience.rollout_info_field
mini_batch_length = experience.step_type.shape[1]
assert mini_batch_length == 1, (
"Only support TrainerConfig.mini_batch_length=1, got %s" %
mini_batch_length)
value_field = info_path + '.value'
candidate_actions_field = info_path + '.candidate_actions'
candidate_action_policy_field = (info_path +
'.candidate_action_policy')
with alf.device(replay_buffer.device):
positions = convert_device(batch_info.positions) # [B]
env_ids = convert_device(batch_info.env_ids) # [B]
if self._reanalyze_ratio > 0:
# Here we assume state and info have similar name scheme.
mcts_state_field = 'state' + info_path[len('rollout_info'):]
r = torch.rand(
experience.step_type.shape[0]) < self._reanalyze_ratio
r_candidate_actions, r_candidate_action_policy, r_values = self._reanalyze(
replay_buffer, env_ids[r], positions[r], mcts_state_field)
# [B]
steps_to_episode_end = replay_buffer.steps_to_episode_end(
positions, env_ids)
# [B]
episode_end_positions = positions + steps_to_episode_end
# [B, unroll_steps+1]
positions = positions.unsqueeze(-1) + torch.arange(
self._num_unroll_steps + 1)
# [B, 1]
env_ids = batch_info.env_ids.unsqueeze(-1)
# [B, 1]
episode_end_positions = episode_end_positions.unsqueeze(-1)
beyond_episode_end = positions > episode_end_positions
positions = torch.min(positions, episode_end_positions)
if self._td_steps >= 0:
values = self._calc_bootstrap_return(replay_buffer, env_ids,
positions, value_field)
else:
values = self._calc_monte_carlo_return(replay_buffer, env_ids,
positions, value_field)
candidate_actions = replay_buffer.get_field(
candidate_actions_field, env_ids, positions)
candidate_action_policy = replay_buffer.get_field(
candidate_action_policy_field, env_ids, positions)
if self._reanalyze_ratio > 0:
if not _is_empty(candidate_actions):
candidate_actions[r] = r_candidate_actions
candidate_action_policy[r] = r_candidate_action_policy
values[r] = r_values
game_overs = ()
if self._train_game_over_function or self._train_reward_function:
game_overs = positions == episode_end_positions
discount = replay_buffer.get_field('discount', env_ids,
positions)
# In the case of discount != 0, the game over may not always be correct
# since the episode is truncated because of TimeLimit or incomplete
# last episode in the replay buffer. There is no way to know for sure
# the future game overs.
game_overs = game_overs & (discount == 0.)
rewards = ()
if self._train_reward_function:
rewards = self._get_reward(replay_buffer, env_ids, positions)
rewards[beyond_episode_end] = 0.
values[game_overs] = 0.
if not self._train_game_over_function:
game_overs = ()
action = replay_buffer.get_field('action', env_ids,
positions[:, :-1])
rollout_info = MuzeroInfo(
action=action,
value=(),
target=ModelTarget(reward=rewards,
action=candidate_actions,
action_policy=candidate_action_policy,
value=values,
game_over=game_overs))
# make the shape to [B, T, ...], where T=1
rollout_info = alf.nest.map_structure(lambda x: x.unsqueeze(1),
rollout_info)
rollout_info = convert_device(rollout_info)
rollout_info = rollout_info._replace(
value=experience.rollout_info.value)
if self._reward_normalizer:
experience = experience._replace(
reward=rollout_info.target.reward[:, :, 0])
return experience._replace(rollout_info=rollout_info)