def _compute_distributional_critic_loss(
sampled_q_t_all: List[tf.Tensor],
q_tm1_all: List[tf.Tensor],
r_t_all: tf.Tensor,
d_t: tf.Tensor,
discount: float,
num_samples: int):
"""Compute loss and sampled Q-values for distributional critics."""
# Compute average logits by first reshaping them and normalizing them
# across atoms.
batch_size = r_t_all.get_shape()[0]
# Cast the additional discount to match the environment discount dtype.
discount = tf.cast(discount, dtype=d_t.dtype)
critic_losses = []
sampled_q_ts = []
for idx, (sampled_q_t_distributions, q_tm1_distribution) in enumerate(
zip(sampled_q_t_all, q_tm1_all)):
# Compute loss for distributional critic for objective c
sampled_logits = tf.reshape(
sampled_q_t_distributions.logits,
[num_samples, batch_size, -1]) # [N, B, A]
sampled_logprobs = tf.math.log_softmax(sampled_logits, axis=-1)
averaged_logits = tf.reduce_logsumexp(sampled_logprobs, axis=0)
# Construct the expected distributional value for bootstrapping.
q_t_distribution = networks.DiscreteValuedDistribution(
values=sampled_q_t_distributions.values, logits=averaged_logits)
# Compute critic distributional loss.
critic_loss = losses.categorical(
q_tm1_distribution, r_t_all[:, idx], discount * d_t,
q_t_distribution)
critic_losses.append(tf.reduce_mean(critic_loss))
# Compute Q-values of sampled actions and reshape to [N, B].
sampled_q_ts.append(tf.reshape(
sampled_q_t_distributions.mean(), (num_samples, -1)))
critic_loss = tf.reduce_mean(critic_losses)
sampled_q_t = tf.stack(sampled_q_ts, axis=-1) # [N, B, C]
return critic_loss, sampled_q_t
def _step(self, sample: reverb.ReplaySample) -> Dict[str, tf.Tensor]:
# Transpose batch and sequence axes, i.e. [B, T, ...] to [T, B, ...].
sample = tf2_utils.batch_to_sequence(sample)
observations = sample.observation
actions = sample.action
rewards = sample.reward
discounts = sample.discount
dtype = rewards.dtype
# Cast the additional discount to match the environment discount dtype.
discount = tf.cast(self._discount, dtype=discounts.dtype)
# Loss cumulants across time. These cannot be python mutable objects.
critic_loss = 0.
policy_loss = 0.
# Each transition induces a policy loss, which we then weight using
# the `policy_loss_coef_t`; shape [B], see https://arxiv.org/abs/2006.15134.
# `policy_loss_coef` is a scalar average of these coefficients across
# the batch and sequence length dimensions.
policy_loss_coef = 0.
per_device_batch_size = actions.shape[1]
# Initialize recurrent states.
critic_state = self._critic_network.initial_state(
per_device_batch_size)
target_critic_state = critic_state
policy_state = self._policy_network.initial_state(
per_device_batch_size)
target_policy_state = policy_state
with tf.GradientTape(persistent=True) as tape:
for t in range(1, self._sequence_length):
o_tm1 = tree.map_structure(operator.itemgetter(t - 1),
observations)
a_tm1 = tree.map_structure(operator.itemgetter(t - 1), actions)
r_t = tree.map_structure(operator.itemgetter(t - 1), rewards)
d_t = tree.map_structure(operator.itemgetter(t - 1), discounts)
o_t = tree.map_structure(operator.itemgetter(t), observations)
if t != 1:
# By only updating the target critic state here we are forcing
# the target critic to ignore observations[0]. Otherwise, the
# target_critic will be unrolled for one more timestep than critic.
# The smaller the sequence length, the more problematic this is: if
# you use RNN on sequences of length 2, you would expect the code to
# never use recurrent connections. But if you don't skip updating the
# target_critic_state on observation[0] here, it won't be the case.
_, target_critic_state = self._target_critic_network(
o_tm1, a_tm1, target_critic_state)
# ========================= Critic learning ============================
q_tm1, next_critic_state = self._critic_network(
o_tm1, a_tm1, critic_state)
target_action_distribution, target_policy_state = self._target_policy_network(
o_t, target_policy_state)
sampled_actions_t = target_action_distribution.sample(
self._num_action_samples_td_learning)
# [N, B, ...]
tiled_o_t = tf2_utils.tile_nested(
o_t, self._num_action_samples_td_learning)
tiled_target_critic_state = tf2_utils.tile_nested(
target_critic_state, self._num_action_samples_td_learning)
# Compute the target critic's Q-value of the sampled actions.
sampled_q_t, _ = snt.BatchApply(self._target_critic_network)(
tiled_o_t, sampled_actions_t, tiled_target_critic_state)
# Compute average logits by first reshaping them to [N, B, A] and then
# normalizing them across atoms.
new_shape = [
self._num_action_samples_td_learning, r_t.shape[0], -1
]
sampled_logits = tf.reshape(sampled_q_t.logits, new_shape)
sampled_logprobs = tf.math.log_softmax(sampled_logits, axis=-1)
averaged_logits = tf.reduce_logsumexp(sampled_logprobs, axis=0)
# Construct the expected distributional value for bootstrapping.
q_t = networks.DiscreteValuedDistribution(
values=sampled_q_t.values, logits=averaged_logits)
critic_loss_t = losses.categorical(q_tm1, r_t, discount * d_t,
q_t)
critic_loss_t = tf.reduce_mean(critic_loss_t)
# ========================= Actor learning =============================
action_distribution_tm1, policy_state = self._policy_network(
o_tm1, policy_state)
q_tm1_mean = q_tm1.mean()
# Compute the estimate of the value function based on
# self._num_action_samples_policy_weight samples from the policy.
tiled_o_tm1 = tf2_utils.tile_nested(
o_tm1, self._num_action_samples_policy_weight)
tiled_critic_state = tf2_utils.tile_nested(
critic_state, self._num_action_samples_policy_weight)
action_tm1 = action_distribution_tm1.sample(
self._num_action_samples_policy_weight)
tiled_z_tm1, _ = snt.BatchApply(self._critic_network)(
tiled_o_tm1, action_tm1, tiled_critic_state)
tiled_v_tm1 = tf.reshape(
tiled_z_tm1.mean(),
[self._num_action_samples_policy_weight, -1])
# Use mean, min, or max to aggregate Q(s, a_i), a_i ~ pi(s) into the
# final estimate of the value function.
if self._baseline_reduce_function == 'mean':
v_tm1_estimate = tf.reduce_mean(tiled_v_tm1, axis=0)
elif self._baseline_reduce_function == 'max':
v_tm1_estimate = tf.reduce_max(tiled_v_tm1, axis=0)
elif self._baseline_reduce_function == 'min':
v_tm1_estimate = tf.reduce_min(tiled_v_tm1, axis=0)
# Assert that action_distribution_tm1 is a batch of multivariate
# distributions (in contrast to e.g. a [batch, action_size] collection
# of 1d distributions).
assert len(action_distribution_tm1.batch_shape) == 1
policy_loss_batch = -action_distribution_tm1.log_prob(a_tm1)
advantage = q_tm1_mean - v_tm1_estimate
if self._policy_improvement_modes == 'exp':
policy_loss_coef_t = tf.math.minimum(
tf.math.exp(advantage / self._beta),
self._ratio_upper_bound)
elif self._policy_improvement_modes == 'binary':
policy_loss_coef_t = tf.cast(advantage > 0, dtype=dtype)
elif self._policy_improvement_modes == 'all':
# Regress against all actions (effectively pure BC).
policy_loss_coef_t = 1.
policy_loss_coef_t = tf.stop_gradient(policy_loss_coef_t)
policy_loss_batch *= policy_loss_coef_t
policy_loss_t = tf.reduce_mean(policy_loss_batch)
critic_state = next_critic_state
critic_loss += critic_loss_t
policy_loss += policy_loss_t
policy_loss_coef += tf.reduce_mean(
policy_loss_coef_t) # For logging.
# Divide by sequence length to get mean losses.
critic_loss /= tf.cast(self._sequence_length, dtype=dtype)
policy_loss /= tf.cast(self._sequence_length, dtype=dtype)
policy_loss_coef /= tf.cast(self._sequence_length, dtype=dtype)
# Compute gradients.
critic_gradients = tape.gradient(
critic_loss, self._critic_network.trainable_variables)
policy_gradients = tape.gradient(
policy_loss, self._policy_network.trainable_variables)
# Delete the tape manually because of the persistent=True flag.
del tape
# Sync gradients across GPUs or TPUs.
ctx = tf.distribute.get_replica_context()
critic_gradients = ctx.all_reduce('mean', critic_gradients)
policy_gradients = ctx.all_reduce('mean', policy_gradients)
# Maybe clip gradients.
if self._clipping:
policy_gradients = tf.clip_by_global_norm(policy_gradients, 40.)[0]
critic_gradients = tf.clip_by_global_norm(critic_gradients, 40.)[0]
# Apply gradients.
self._critic_optimizer.apply(critic_gradients,
self._critic_network.trainable_variables)
self._policy_optimizer.apply(policy_gradients,
self._policy_network.trainable_variables)
source_variables = (self._critic_network.variables +
self._policy_network.variables)
target_variables = (self._target_critic_network.variables +
self._target_policy_network.variables)
# Make online -> target network update ops.
if tf.math.mod(self._num_steps, self._target_update_period) == 0:
for src, dest in zip(source_variables, target_variables):
dest.assign(src)
self._num_steps.assign_add(1)
return {
'critic_loss': critic_loss,
'policy_loss': policy_loss,
'policy_loss_coef': policy_loss_coef,
}
def _step(self) -> types.NestedTensor:
# Update target network.
online_policy_variables = self._policy_network.variables
target_policy_variables = self._target_policy_network.variables
online_critic_variables = (
*self._observation_network.variables,
*self._critic_network.variables,
)
target_critic_variables = (
*self._target_observation_network.variables,
*self._target_critic_network.variables,
)
# Make online policy -> target policy network update ops.
if tf.math.mod(self._num_steps,
self._target_policy_update_period) == 0:
for src, dest in zip(online_policy_variables,
target_policy_variables):
dest.assign(src)
# Make online critic -> target critic network update ops.
if tf.math.mod(self._num_steps,
self._target_critic_update_period) == 0:
for src, dest in zip(online_critic_variables,
target_critic_variables):
dest.assign(src)
self._num_steps.assign_add(1)
# Get data from replay (dropping extras if any). Note there is no
# extra data here because we do not insert any into Reverb.
inputs = next(self._iterator)
o_tm1, a_tm1, r_t, d_t, o_t = inputs.data
# Get batch size and scalar dtype.
batch_size = r_t.shape[0]
# Cast the additional discount to match the environment discount dtype.
discount = tf.cast(self._discount, dtype=d_t.dtype)
with tf.GradientTape(persistent=True) as tape:
# Maybe transform the observation before feeding into policy and critic.
# Transforming the observations this way at the start of the learning
# step effectively means that the policy and critic share observation
# network weights.
o_tm1 = self._observation_network(o_tm1)
# This stop_gradient prevents gradients to propagate into the target
# observation network. In addition, since the online policy network is
# evaluated at o_t, this also means the policy loss does not influence
# the observation network training.
o_t = tf.stop_gradient(self._target_observation_network(o_t))
# Get online and target action distributions from policy networks.
online_action_distribution = self._policy_network(o_t)
target_action_distribution = self._target_policy_network(o_t)
# Sample actions to evaluate policy; of size [N, B, ...].
sampled_actions = target_action_distribution.sample(
self._num_samples)
# Tile embedded observations to feed into the target critic network.
# Note: this is more efficient than tiling before the embedding layer.
tiled_o_t = tf2_utils.tile_tensor(o_t,
self._num_samples) # [N, B, ...]
# Compute target-estimated distributional value of sampled actions at o_t.
sampled_q_t_distributions = self._target_critic_network(
# Merge batch dimensions; to shape [N*B, ...].
snt.merge_leading_dims(tiled_o_t, num_dims=2),
snt.merge_leading_dims(sampled_actions, num_dims=2))
# Compute average logits by first reshaping them and normalizing them
# across atoms.
new_shape = [self._num_samples, batch_size, -1] # [N, B, A]
sampled_logits = tf.reshape(sampled_q_t_distributions.logits,
new_shape)
sampled_logprobs = tf.math.log_softmax(sampled_logits, axis=-1)
averaged_logits = tf.reduce_logsumexp(sampled_logprobs, axis=0)
# Construct the expected distributional value for bootstrapping.
q_t_distribution = networks.DiscreteValuedDistribution(
values=sampled_q_t_distributions.values,
logits=averaged_logits)
# Compute online critic value distribution of a_tm1 in state o_tm1.
q_tm1_distribution = self._critic_network(o_tm1, a_tm1)
# Compute critic distributional loss.
critic_loss = losses.categorical(q_tm1_distribution, r_t,
discount * d_t, q_t_distribution)
critic_loss = tf.reduce_mean(critic_loss)
# Compute Q-values of sampled actions and reshape to [N, B].
sampled_q_values = sampled_q_t_distributions.mean()
sampled_q_values = tf.reshape(sampled_q_values,
(self._num_samples, -1))
# Compute MPO policy loss.
policy_loss, policy_stats = self._policy_loss_module(
online_action_distribution=online_action_distribution,
target_action_distribution=target_action_distribution,
actions=sampled_actions,
q_values=sampled_q_values)
# For clarity, explicitly define which variables are trained by which loss.
critic_trainable_variables = (
# In this agent, the critic loss trains the observation network.
self._observation_network.trainable_variables +
self._critic_network.trainable_variables)
policy_trainable_variables = self._policy_network.trainable_variables
# The following are the MPO dual variables, stored in the loss module.
dual_trainable_variables = self._policy_loss_module.trainable_variables
# Compute gradients.
critic_gradients = tape.gradient(critic_loss,
critic_trainable_variables)
policy_gradients, dual_gradients = tape.gradient(
policy_loss,
(policy_trainable_variables, dual_trainable_variables))
# Delete the tape manually because of the persistent=True flag.
del tape
# Maybe clip gradients.
if self._clipping:
policy_gradients = tuple(
tf.clip_by_global_norm(policy_gradients, 40.)[0])
critic_gradients = tuple(
tf.clip_by_global_norm(critic_gradients, 40.)[0])
# Apply gradients.
self._critic_optimizer.apply(critic_gradients,
critic_trainable_variables)
self._policy_optimizer.apply(policy_gradients,
policy_trainable_variables)
self._dual_optimizer.apply(dual_gradients, dual_trainable_variables)
# Losses to track.
fetches = {
'critic_loss': critic_loss,
'policy_loss': policy_loss,
}
fetches.update(policy_stats) # Log MPO stats.
return fetches
def _step(self) -> types.NestedTensor:
# Update target network.
online_policy_variables = self._policy_network.variables
target_policy_variables = self._target_policy_network.variables
online_critic_variables = (
*self._observation_network.variables,
*self._critic_network.variables,
)
target_critic_variables = (
*self._target_observation_network.variables,
*self._target_critic_network.variables,
)
# Make online policy -> target policy network update ops.
if tf.math.mod(self._num_steps,
self._target_policy_update_period) == 0:
for src, dest in zip(online_policy_variables,
target_policy_variables):
dest.assign(src)
# Make online critic -> target critic network update ops.
if tf.math.mod(self._num_steps,
self._target_critic_update_period) == 0:
for src, dest in zip(online_critic_variables,
target_critic_variables):
dest.assign(src)
self._num_steps.assign_add(1)
# Get data from replay (dropping extras if any). Note there is no
# extra data here because we do not insert any into Reverb.
inputs = next(self._iterator)
o_tm1, a_tm1, r_t, d_t, o_t = inputs.data
# Get batch size and scalar dtype.
batch_size = r_t.shape[0]
# Cast the additional discount to match the environment discount dtype.
discount = tf.cast(self._discount, dtype=d_t.dtype)
with tf.GradientTape(persistent=True) as tape:
# Maybe transform the observation before feeding into policy and critic.
# Transforming the observations this way at the start of the learning
# step effectively means that the policy and critic share observation
# network weights.
o_tm1 = self._observation_network(o_tm1)
# This stop_gradient prevents gradients to propagate into the target
# observation network. In addition, since the online policy network is
# evaluated at o_t, this also means the policy loss does not influence
# the observation network training.
o_t = tf.stop_gradient(self._target_observation_network(o_t))
# Get online and target action distributions from policy networks.
online_action_distribution = self._policy_network(o_t)
target_action_distribution = self._target_policy_network(o_t)
# Sample actions to evaluate policy; of size [N, B, ...].
sampled_actions = target_action_distribution.sample(
self._num_samples)
# Tile embedded observations to feed into the target critic network.
# Note: this is more efficient than tiling before the embedding layer.
tiled_o_t = tf2_utils.tile_tensor(o_t,
self._num_samples) # [N, B, ...]
# Compute target-estimated distributional value of sampled actions at o_t.
sampled_q_t_all = self._target_critic_network(
# Merge batch dimensions; to shape [N*B, ...].
snt.merge_leading_dims(tiled_o_t, num_dims=2),
snt.merge_leading_dims(sampled_actions, num_dims=2))
# Compute online critic value distribution of a_tm1 in state o_tm1.
q_tm1_all = self._critic_network(o_tm1, a_tm1)
# Compute rewards for objectives with defined reward_fn
reward_stats = {}
r_t_all = []
for objective in self._objectives:
if hasattr(objective, 'reward_fn'):
r = objective.reward_fn(o_tm1, a_tm1, r_t)
reward_stats['{}_reward'.format(
objective.name)] = tf.reduce_mean(r)
r_t_all.append(r)
r_t_all = tf.stack(r_t_all, axis=-1)
r_t_all.get_shape().assert_has_rank(2) # [B, C]
if isinstance(sampled_q_t_all, list): # Distributional critics
# Compute average logits by first reshaping them and normalizing them
# across atoms.
critic_losses = []
sampled_q_ts = []
for idx, (sampled_q_t_distributions,
q_tm1_distribution) in enumerate(
zip(sampled_q_t_all, q_tm1_all)):
# Compute loss for distributional critic for objective c
sampled_logits = tf.reshape(
sampled_q_t_distributions.logits,
[self._num_samples, batch_size, -1]) # [N, B, A]
sampled_logprobs = tf.math.log_softmax(sampled_logits,
axis=-1)
averaged_logits = tf.reduce_logsumexp(sampled_logprobs,
axis=0)
# Construct the expected distributional value for bootstrapping.
q_t_distribution = networks.DiscreteValuedDistribution(
values=sampled_q_t_distributions.values,
logits=averaged_logits)
# Compute critic distributional loss.
critic_loss = losses.categorical(q_tm1_distribution,
r_t_all[:, idx],
discount * d_t,
q_t_distribution)
critic_losses.append(tf.reduce_mean(critic_loss))
# Compute Q-values of sampled actions and reshape to [N, B].
sampled_q_ts.append(
tf.reshape(sampled_q_t_distributions.mean(),
(self._num_samples, -1)))
critic_loss = tf.reduce_mean(critic_losses)
sampled_q_t = tf.stack(sampled_q_ts, axis=-1) # [N, B, C]
else:
# Reshape Q-value samples back to original batch dimensions and average
# them to compute the TD-learning bootstrap target.
sampled_q_t = tf.reshape(sampled_q_t_all,
(self._num_samples, batch_size,
self._num_critic_heads)) # [N,B,C]
q_t = tf.reduce_mean(sampled_q_t, axis=0) # [B, C]
# Flatten q_t and q_tm1; necessary for trfl.td_learning
q_t = tf.reshape(q_t, [-1]) # [B*C]
q_tm1 = tf.reshape(q_tm1_all, [-1]) # [B*C]
# Flatten r_t_all; necessary for trfl.td_learning
r_t_all = tf.reshape(r_t_all, [-1]) # [B*C]
# Broadcast and then flatten d_t, to match shape of q_t and q_tm1
d_t = tf.tile(d_t, [self._num_critic_heads]) # [B*C]
# Critic loss.
critic_loss = trfl.td_learning(q_tm1, r_t_all, discount * d_t,
q_t).loss
critic_loss = tf.reduce_mean(critic_loss)
# Add sampled Q-values for objectives with defined objective_fn
sampled_q_idx = 0
sampled_q_t_k = []
for objective in self._objectives:
if hasattr(objective, 'reward_fn'):
sampled_q_t_k.append(
tf.stop_gradient(sampled_q_t[..., sampled_q_idx]))
sampled_q_idx += 1
if hasattr(objective, 'objective_fn'):
sampled_q_t_k.append(
tf.stop_gradient(
objective.objective_fn(sampled_actions,
sampled_q_t)))
sampled_q_t_k = tf.stack(sampled_q_t_k, axis=-1) # [N, B, K]
# Compute MPO policy loss.
policy_loss, policy_stats = self._policy_loss_module(
online_action_distribution=online_action_distribution,
target_action_distribution=target_action_distribution,
actions=sampled_actions,
q_values=sampled_q_t_k)
# For clarity, explicitly define which variables are trained by which loss.
critic_trainable_variables = (
# In this agent, the critic loss trains the observation network.
self._observation_network.trainable_variables +
self._critic_network.trainable_variables)
policy_trainable_variables = self._policy_network.trainable_variables
# The following are the MPO dual variables, stored in the loss module.
dual_trainable_variables = self._policy_loss_module.trainable_variables
# Compute gradients.
critic_gradients = tape.gradient(critic_loss,
critic_trainable_variables)
policy_gradients, dual_gradients = tape.gradient(
policy_loss,
(policy_trainable_variables, dual_trainable_variables))
# Delete the tape manually because of the persistent=True flag.
del tape
# Maybe clip gradients.
if self._clipping:
policy_gradients = tuple(
tf.clip_by_global_norm(policy_gradients, 40.)[0])
critic_gradients = tuple(
tf.clip_by_global_norm(critic_gradients, 40.)[0])
# Apply gradients.
self._critic_optimizer.apply(critic_gradients,
critic_trainable_variables)
self._policy_optimizer.apply(policy_gradients,
policy_trainable_variables)
self._dual_optimizer.apply(dual_gradients, dual_trainable_variables)
# Losses to track.
fetches = {
'critic_loss': critic_loss,
'policy_loss': policy_loss,
}
fetches.update(policy_stats) # Log MPO stats.
fetches.update(reward_stats) # Log reward stats.
return fetches
