def appo_surrogate_loss(
policy: Policy, model: ModelV2, dist_class: Type[TFActionDistribution],
train_batch: SampleBatch) -> Union[TensorType, List[TensorType]]:
"""Constructs the loss for APPO.
With IS modifications and V-trace for Advantage Estimation.
Args:
policy (Policy): The Policy to calculate the loss for.
model (ModelV2): The Model to calculate the loss for.
dist_class (Type[ActionDistribution]): The action distr. class.
train_batch (SampleBatch): The training data.
Returns:
Union[TensorType, List[TensorType]]: A single loss tensor or a list
of loss tensors.
"""
model_out, _ = model.from_batch(train_batch)
action_dist = dist_class(model_out, model)
if isinstance(policy.action_space, gym.spaces.Discrete):
is_multidiscrete = False
output_hidden_shape = [policy.action_space.n]
elif isinstance(policy.action_space,
gym.spaces.multi_discrete.MultiDiscrete):
is_multidiscrete = True
output_hidden_shape = policy.action_space.nvec.astype(np.int32)
else:
is_multidiscrete = False
output_hidden_shape = 1
# TODO: (sven) deprecate this when trajectory view API gets activated.
def make_time_major(*args, **kw):
return _make_time_major(policy, train_batch.get("seq_lens"), *args,
**kw)
actions = train_batch[SampleBatch.ACTIONS]
dones = train_batch[SampleBatch.DONES]
rewards = train_batch[SampleBatch.REWARDS]
behaviour_logits = train_batch[SampleBatch.ACTION_DIST_INPUTS]
target_model_out, _ = policy.target_model.from_batch(train_batch)
prev_action_dist = dist_class(behaviour_logits, policy.model)
values = policy.model.value_function()
values_time_major = make_time_major(values)
policy.model_vars = policy.model.variables()
policy.target_model_vars = policy.target_model.variables()
if policy.is_recurrent():
max_seq_len = tf.reduce_max(train_batch["seq_lens"]) - 1
mask = tf.sequence_mask(train_batch["seq_lens"], max_seq_len)
mask = tf.reshape(mask, [-1])
mask = make_time_major(mask, drop_last=policy.config["vtrace"])
def reduce_mean_valid(t):
return tf.reduce_mean(tf.boolean_mask(t, mask))
else:
reduce_mean_valid = tf.reduce_mean
if policy.config["vtrace"]:
logger.debug("Using V-Trace surrogate loss (vtrace=True)")
# Prepare actions for loss.
loss_actions = actions if is_multidiscrete else tf.expand_dims(actions,
axis=1)
old_policy_behaviour_logits = tf.stop_gradient(target_model_out)
old_policy_action_dist = dist_class(old_policy_behaviour_logits, model)
# Prepare KL for Loss
mean_kl = make_time_major(old_policy_action_dist.multi_kl(action_dist),
drop_last=True)
unpacked_behaviour_logits = tf.split(behaviour_logits,
output_hidden_shape,
axis=1)
unpacked_old_policy_behaviour_logits = tf.split(
old_policy_behaviour_logits, output_hidden_shape, axis=1)
# Compute vtrace on the CPU for better perf.
with tf.device("/cpu:0"):
vtrace_returns = vtrace.multi_from_logits(
behaviour_policy_logits=make_time_major(
unpacked_behaviour_logits, drop_last=True),
target_policy_logits=make_time_major(
unpacked_old_policy_behaviour_logits, drop_last=True),
actions=tf.unstack(make_time_major(loss_actions,
drop_last=True),
axis=2),
discounts=tf.cast(
~make_time_major(tf.cast(dones, tf.bool), drop_last=True),
tf.float32) * policy.config["gamma"],
rewards=make_time_major(rewards, drop_last=True),
values=values_time_major[:-1], # drop-last=True
bootstrap_value=values_time_major[-1],
dist_class=Categorical if is_multidiscrete else dist_class,
model=model,
clip_rho_threshold=tf.cast(
policy.config["vtrace_clip_rho_threshold"], tf.float32),
clip_pg_rho_threshold=tf.cast(
policy.config["vtrace_clip_pg_rho_threshold"], tf.float32),
)
actions_logp = make_time_major(action_dist.logp(actions),
drop_last=True)
prev_actions_logp = make_time_major(prev_action_dist.logp(actions),
drop_last=True)
old_policy_actions_logp = make_time_major(
old_policy_action_dist.logp(actions), drop_last=True)
is_ratio = tf.clip_by_value(
tf.math.exp(prev_actions_logp - old_policy_actions_logp), 0.0, 2.0)
logp_ratio = is_ratio * tf.exp(actions_logp - prev_actions_logp)
policy._is_ratio = is_ratio
advantages = vtrace_returns.pg_advantages
surrogate_loss = tf.minimum(
advantages * logp_ratio,
advantages *
tf.clip_by_value(logp_ratio, 1 - policy.config["clip_param"],
1 + policy.config["clip_param"]))
action_kl = tf.reduce_mean(mean_kl, axis=0) \
if is_multidiscrete else mean_kl
mean_kl = reduce_mean_valid(action_kl)
mean_policy_loss = -reduce_mean_valid(surrogate_loss)
# The value function loss.
delta = values_time_major[:-1] - vtrace_returns.vs
value_targets = vtrace_returns.vs
mean_vf_loss = 0.5 * reduce_mean_valid(tf.math.square(delta))
# The entropy loss.
actions_entropy = make_time_major(action_dist.multi_entropy(),
drop_last=True)
mean_entropy = reduce_mean_valid(actions_entropy)
else:
logger.debug("Using PPO surrogate loss (vtrace=False)")
# Prepare KL for Loss
mean_kl = make_time_major(prev_action_dist.multi_kl(action_dist))
logp_ratio = tf.math.exp(
make_time_major(action_dist.logp(actions)) -
make_time_major(prev_action_dist.logp(actions)))
advantages = make_time_major(train_batch[Postprocessing.ADVANTAGES])
surrogate_loss = tf.minimum(
advantages * logp_ratio,
advantages *
tf.clip_by_value(logp_ratio, 1 - policy.config["clip_param"],
1 + policy.config["clip_param"]))
action_kl = tf.reduce_mean(mean_kl, axis=0) \
if is_multidiscrete else mean_kl
mean_kl = reduce_mean_valid(action_kl)
mean_policy_loss = -reduce_mean_valid(surrogate_loss)
# The value function loss.
value_targets = make_time_major(
train_batch[Postprocessing.VALUE_TARGETS])
delta = values_time_major - value_targets
mean_vf_loss = 0.5 * reduce_mean_valid(tf.math.square(delta))
# The entropy loss.
mean_entropy = reduce_mean_valid(
make_time_major(action_dist.multi_entropy()))
# The summed weighted loss
total_loss = mean_policy_loss + \
mean_vf_loss * policy.config["vf_loss_coeff"] - \
mean_entropy * policy.config["entropy_coeff"]
# Optional additional KL Loss
if policy.config["use_kl_loss"]:
total_loss += policy.kl_coeff * mean_kl
policy._total_loss = total_loss
policy._mean_policy_loss = mean_policy_loss
policy._mean_kl = mean_kl
policy._mean_vf_loss = mean_vf_loss
policy._mean_entropy = mean_entropy
policy._value_targets = value_targets
# Store stats in policy for stats_fn.
return total_loss
def __init__(self,
actions,
prev_actions_logp,
actions_logp,
old_policy_actions_logp,
action_kl,
actions_entropy,
dones,
behaviour_logits,
old_policy_behaviour_logits,
target_logits,
discount,
rewards,
values,
bootstrap_value,
dist_class,
model,
valid_mask,
vf_loss_coeff=0.5,
entropy_coeff=0.01,
clip_rho_threshold=1.0,
clip_pg_rho_threshold=1.0,
clip_param=0.3,
cur_kl_coeff=None,
use_kl_loss=False):
"""APPO Loss, with IS modifications and V-trace for Advantage Estimation
VTraceLoss takes tensors of shape [T, B, ...], where `B` is the
batch_size. The reason we need to know `B` is for V-trace to properly
handle episode cut boundaries.
Arguments:
actions: An int|float32 tensor of shape [T, B, logit_dim].
prev_actions_logp: A float32 tensor of shape [T, B].
actions_logp: A float32 tensor of shape [T, B].
old_policy_actions_logp: A float32 tensor of shape [T, B].
action_kl: A float32 tensor of shape [T, B].
actions_entropy: A float32 tensor of shape [T, B].
dones: A bool tensor of shape [T, B].
behaviour_logits: A float32 tensor of shape [T, B, logit_dim].
old_policy_behaviour_logits: A float32 tensor of shape
[T, B, logit_dim].
target_logits: A float32 tensor of shape [T, B, logit_dim].
discount: A float32 scalar.
rewards: A float32 tensor of shape [T, B].
values: A float32 tensor of shape [T, B].
bootstrap_value: A float32 tensor of shape [B].
dist_class: action distribution class for logits.
model: backing ModelV2 instance
valid_mask: A bool tensor of valid RNN input elements (#2992).
vf_loss_coeff (float): Coefficient of the value function loss.
entropy_coeff (float): Coefficient of the entropy regularizer.
clip_param (float): Clip parameter.
cur_kl_coeff (float): Coefficient for KL loss.
use_kl_loss (bool): If true, use KL loss.
"""
def reduce_mean_valid(t):
return tf.reduce_mean(tf.boolean_mask(t, valid_mask))
# Compute vtrace on the CPU for better perf.
with tf.device("/cpu:0"):
self.vtrace_returns = vtrace.multi_from_logits(
behaviour_policy_logits=behaviour_logits,
target_policy_logits=old_policy_behaviour_logits,
actions=tf.unstack(actions, axis=2),
discounts=tf.to_float(~dones) * discount,
rewards=rewards,
values=values,
bootstrap_value=bootstrap_value,
dist_class=dist_class,
model=model,
clip_rho_threshold=tf.cast(clip_rho_threshold, tf.float32),
clip_pg_rho_threshold=tf.cast(clip_pg_rho_threshold,
tf.float32))
self.is_ratio = tf.clip_by_value(
tf.exp(prev_actions_logp - old_policy_actions_logp), 0.0, 2.0)
logp_ratio = self.is_ratio * tf.exp(actions_logp - prev_actions_logp)
advantages = self.vtrace_returns.pg_advantages
surrogate_loss = tf.minimum(
advantages * logp_ratio,
advantages * tf.clip_by_value(logp_ratio, 1 - clip_param,
1 + clip_param))
self.mean_kl = reduce_mean_valid(action_kl)
self.pi_loss = -reduce_mean_valid(surrogate_loss)
# The baseline loss
delta = values - self.vtrace_returns.vs
self.value_targets = self.vtrace_returns.vs
self.vf_loss = 0.5 * reduce_mean_valid(tf.square(delta))
# The entropy loss
self.entropy = reduce_mean_valid(actions_entropy)
# The summed weighted loss
self.total_loss = (self.pi_loss + self.vf_loss * vf_loss_coeff -
self.entropy * entropy_coeff)
# Optional additional KL Loss
if use_kl_loss:
self.total_loss += cur_kl_coeff * self.mean_kl
def __init__(self,
actions,
actions_logp,
actions_entropy,
dones,
behaviour_action_logp,
behaviour_logits,
target_logits,
discount,
rewards,
values,
bootstrap_value,
dist_class,
model,
valid_mask,
config,
vf_loss_coeff=0.5,
entropy_coeff=0.01,
clip_rho_threshold=1.0,
clip_pg_rho_threshold=1.0):
"""Policy gradient loss with vtrace importance weighting.
VTraceLoss takes tensors of shape [T, B, ...], where `B` is the
batch_size. The reason we need to know `B` is for V-trace to properly
handle episode cut boundaries.
Args:
actions: An int|float32 tensor of shape [T, B, ACTION_SPACE].
actions_logp: A float32 tensor of shape [T, B].
actions_entropy: A float32 tensor of shape [T, B].
dones: A bool tensor of shape [T, B].
behaviour_action_logp: Tensor of shape [T, B].
behaviour_logits: A list with length of ACTION_SPACE of float32
tensors of shapes
[T, B, ACTION_SPACE[0]],
...,
[T, B, ACTION_SPACE[-1]]
target_logits: A list with length of ACTION_SPACE of float32
tensors of shapes
[T, B, ACTION_SPACE[0]],
...,
[T, B, ACTION_SPACE[-1]]
discount: A float32 scalar.
rewards: A float32 tensor of shape [T, B].
values: A float32 tensor of shape [T, B].
bootstrap_value: A float32 tensor of shape [B].
dist_class: action distribution class for logits.
valid_mask: A bool tensor of valid RNN input elements (#2992).
config: Trainer config dict.
"""
# Compute vtrace on the CPU for better perf.
with tf.device("/cpu:0"):
self.vtrace_returns = vtrace.multi_from_logits(
behaviour_action_log_probs=behaviour_action_logp,
behaviour_policy_logits=behaviour_logits,
target_policy_logits=target_logits,
actions=tf.unstack(actions, axis=2),
discounts=tf.to_float(~dones) * discount,
rewards=rewards,
values=values,
bootstrap_value=bootstrap_value,
dist_class=dist_class,
model=model,
clip_rho_threshold=tf.cast(clip_rho_threshold, tf.float32),
clip_pg_rho_threshold=tf.cast(clip_pg_rho_threshold,
tf.float32))
self.value_targets = self.vtrace_returns.vs
# The policy gradients loss
self.pi_loss = -tf.reduce_sum(
tf.boolean_mask(actions_logp * self.vtrace_returns.pg_advantages,
valid_mask))
# The baseline loss
delta = tf.boolean_mask(values - self.vtrace_returns.vs, valid_mask)
self.vf_loss = 0.5 * tf.reduce_sum(tf.square(delta))
# The entropy loss
self.entropy = tf.reduce_sum(
tf.boolean_mask(actions_entropy, valid_mask))
# The summed weighted loss
self.total_loss = (self.pi_loss + self.vf_loss * vf_loss_coeff -
self.entropy * entropy_coeff)
def loss(
self,
model: Union[ModelV2, "tf.keras.Model"],
dist_class: Type[TFActionDistribution],
train_batch: SampleBatch,
) -> Union[TensorType, List[TensorType]]:
model_out, _ = model(train_batch)
action_dist = dist_class(model_out, model)
if isinstance(self.action_space, gym.spaces.Discrete):
is_multidiscrete = False
output_hidden_shape = [self.action_space.n]
elif isinstance(self.action_space, gym.spaces.multi_discrete.MultiDiscrete):
is_multidiscrete = True
output_hidden_shape = self.action_space.nvec.astype(np.int32)
else:
is_multidiscrete = False
output_hidden_shape = 1
# TODO: (sven) deprecate this when trajectory view API gets activated.
def make_time_major(*args, **kw):
return _make_time_major(
self, train_batch.get(SampleBatch.SEQ_LENS), *args, **kw
)
actions = train_batch[SampleBatch.ACTIONS]
dones = train_batch[SampleBatch.DONES]
rewards = train_batch[SampleBatch.REWARDS]
behaviour_logits = train_batch[SampleBatch.ACTION_DIST_INPUTS]
target_model_out, _ = self.target_model(train_batch)
prev_action_dist = dist_class(behaviour_logits, self.model)
values = self.model.value_function()
values_time_major = make_time_major(values)
self.model_vars = self.model.variables()
self.target_model_vars = self.target_model.variables()
if self.is_recurrent():
max_seq_len = tf.reduce_max(train_batch[SampleBatch.SEQ_LENS])
mask = tf.sequence_mask(train_batch[SampleBatch.SEQ_LENS], max_seq_len)
mask = tf.reshape(mask, [-1])
mask = make_time_major(mask, drop_last=self.config["vtrace"])
def reduce_mean_valid(t):
return tf.reduce_mean(tf.boolean_mask(t, mask))
else:
reduce_mean_valid = tf.reduce_mean
if self.config["vtrace"]:
drop_last = self.config["vtrace_drop_last_ts"]
logger.debug(
"Using V-Trace surrogate loss (vtrace=True; "
f"drop_last={drop_last})"
)
# Prepare actions for loss.
loss_actions = (
actions if is_multidiscrete else tf.expand_dims(actions, axis=1)
)
old_policy_behaviour_logits = tf.stop_gradient(target_model_out)
old_policy_action_dist = dist_class(old_policy_behaviour_logits, model)
# Prepare KL for Loss
mean_kl = make_time_major(
old_policy_action_dist.multi_kl(action_dist), drop_last=drop_last
)
unpacked_behaviour_logits = tf.split(
behaviour_logits, output_hidden_shape, axis=1
)
unpacked_old_policy_behaviour_logits = tf.split(
old_policy_behaviour_logits, output_hidden_shape, axis=1
)
# Compute vtrace on the CPU for better perf.
with tf.device("/cpu:0"):
vtrace_returns = vtrace.multi_from_logits(
behaviour_policy_logits=make_time_major(
unpacked_behaviour_logits, drop_last=drop_last
),
target_policy_logits=make_time_major(
unpacked_old_policy_behaviour_logits, drop_last=drop_last
),
actions=tf.unstack(
make_time_major(loss_actions, drop_last=drop_last), axis=2
),
discounts=tf.cast(
~make_time_major(
tf.cast(dones, tf.bool), drop_last=drop_last
),
tf.float32,
)
* self.config["gamma"],
rewards=make_time_major(rewards, drop_last=drop_last),
values=values_time_major[:-1]
if drop_last
else values_time_major,
bootstrap_value=values_time_major[-1],
dist_class=Categorical if is_multidiscrete else dist_class,
model=model,
clip_rho_threshold=tf.cast(
self.config["vtrace_clip_rho_threshold"], tf.float32
),
clip_pg_rho_threshold=tf.cast(
self.config["vtrace_clip_pg_rho_threshold"], tf.float32
),
)
actions_logp = make_time_major(
action_dist.logp(actions), drop_last=drop_last
)
prev_actions_logp = make_time_major(
prev_action_dist.logp(actions), drop_last=drop_last
)
old_policy_actions_logp = make_time_major(
old_policy_action_dist.logp(actions), drop_last=drop_last
)
is_ratio = tf.clip_by_value(
tf.math.exp(prev_actions_logp - old_policy_actions_logp), 0.0, 2.0
)
logp_ratio = is_ratio * tf.exp(actions_logp - prev_actions_logp)
self._is_ratio = is_ratio
advantages = vtrace_returns.pg_advantages
surrogate_loss = tf.minimum(
advantages * logp_ratio,
advantages
* tf.clip_by_value(
logp_ratio,
1 - self.config["clip_param"],
1 + self.config["clip_param"],
),
)
action_kl = (
tf.reduce_mean(mean_kl, axis=0) if is_multidiscrete else mean_kl
)
mean_kl_loss = reduce_mean_valid(action_kl)
mean_policy_loss = -reduce_mean_valid(surrogate_loss)
# The value function loss.
if drop_last:
delta = values_time_major[:-1] - vtrace_returns.vs
else:
delta = values_time_major - vtrace_returns.vs
value_targets = vtrace_returns.vs
mean_vf_loss = 0.5 * reduce_mean_valid(tf.math.square(delta))
# The entropy loss.
actions_entropy = make_time_major(
action_dist.multi_entropy(), drop_last=True
)
mean_entropy = reduce_mean_valid(actions_entropy)
else:
logger.debug("Using PPO surrogate loss (vtrace=False)")
# Prepare KL for Loss
mean_kl = make_time_major(prev_action_dist.multi_kl(action_dist))
logp_ratio = tf.math.exp(
make_time_major(action_dist.logp(actions))
- make_time_major(prev_action_dist.logp(actions))
)
advantages = make_time_major(train_batch[Postprocessing.ADVANTAGES])
surrogate_loss = tf.minimum(
advantages * logp_ratio,
advantages
* tf.clip_by_value(
logp_ratio,
1 - self.config["clip_param"],
1 + self.config["clip_param"],
),
)
action_kl = (
tf.reduce_mean(mean_kl, axis=0) if is_multidiscrete else mean_kl
)
mean_kl_loss = reduce_mean_valid(action_kl)
mean_policy_loss = -reduce_mean_valid(surrogate_loss)
# The value function loss.
value_targets = make_time_major(
train_batch[Postprocessing.VALUE_TARGETS]
)
delta = values_time_major - value_targets
mean_vf_loss = 0.5 * reduce_mean_valid(tf.math.square(delta))
# The entropy loss.
mean_entropy = reduce_mean_valid(
make_time_major(action_dist.multi_entropy())
)
# The summed weighted loss.
total_loss = mean_policy_loss - mean_entropy * self.entropy_coeff
# Optional KL loss.
if self.config["use_kl_loss"]:
total_loss += self.kl_coeff * mean_kl_loss
# Optional vf loss (or in a separate term due to separate
# optimizers/networks).
loss_wo_vf = total_loss
if not self.config["_separate_vf_optimizer"]:
total_loss += mean_vf_loss * self.config["vf_loss_coeff"]
# Store stats in policy for stats_fn.
self._total_loss = total_loss
self._loss_wo_vf = loss_wo_vf
self._mean_policy_loss = mean_policy_loss
# Backward compatibility: Deprecate policy._mean_kl.
self._mean_kl_loss = self._mean_kl = mean_kl_loss
self._mean_vf_loss = mean_vf_loss
self._mean_entropy = mean_entropy
self._value_targets = value_targets
# Return one total loss or two losses: vf vs rest (policy + kl).
if self.config["_separate_vf_optimizer"]:
return loss_wo_vf, mean_vf_loss
else:
return total_loss
