def initialize(self, custom_getter):
super(PGModel, self).initialize(custom_getter)
# Baseline
if self.baseline_spec is None:
assert self.baseline_mode is None
elif all(name in self.states_spec for name in self.baseline_spec):
# Implies AggregatedBaseline.
assert self.baseline_mode == 'states'
self.baseline = AggregatedBaseline(baselines=self.baseline_spec)
else:
assert self.baseline_mode is not None
self.baseline = Baseline.from_spec(
spec=self.baseline_spec,
kwargs=dict(summary_labels=self.summary_labels))
# Baseline optimizer
if self.baseline_optimizer_spec is not None:
assert self.baseline_mode is not None
self.baseline_optimizer = Optimizer.from_spec(
spec=self.baseline_optimizer_spec)
# TODO: Baseline internal states !!! (see target_network q_model)
# Reward estimation
self.fn_reward_estimation = tf.make_template(
name_='reward-estimation',
func_=self.tf_reward_estimation,
custom_getter_=custom_getter)
# Baseline loss
self.fn_baseline_loss = tf.make_template(name_='baseline-loss',
func_=self.tf_baseline_loss,
custom_getter_=custom_getter)
def __init__(self, states_spec, actions_spec, network_spec, config):
# Baseline mode
assert config.baseline_mode is None or config.baseline_mode in (
'states', 'network')
self.baseline_mode = config.baseline_mode
with tf.name_scope(name=config.scope):
# Baseline
if config.baseline is None:
assert self.baseline_mode is None
self.baseline = None
elif all(name in states_spec for name in config.baseline):
# Implies AggregatedBaseline
assert self.baseline_mode == 'states'
self.baseline = AggregatedBaseline(baselines=config.baseline)
else:
assert self.baseline_mode is not None
self.baseline = Baseline.from_spec(
spec=config.baseline,
kwargs=dict(summary_labels=config.summary_labels))
# Baseline optimizer
if config.baseline_optimizer is None:
self.baseline_optimizer = None
else:
assert self.baseline_mode is not None
self.baseline_optimizer = Optimizer.from_spec(
spec=config.baseline_optimizer)
# Generalized advantage function
assert config.gae_lambda is None or (
0.0 <= config.gae_lambda <= 1.0 and self.baseline_mode is not None)
self.gae_lambda = config.gae_lambda
super(PGModel, self).__init__(states_spec=states_spec,
actions_spec=actions_spec,
network_spec=network_spec,
config=config)
def initialize(self, custom_getter):
super(PGModel, self).initialize(custom_getter)
# Baseline
if self.baseline is None:
assert self.baseline_mode is None
self.baseline = None
elif all(name in self.states_spec for name in self.baseline):
# Implies AggregatedBaseline.
assert self.baseline_mode == 'states'
self.baseline = AggregatedBaseline(baselines=self.baseline)
else:
assert self.baseline_mode is not None
self.baseline = Baseline.from_spec(
spec=self.baseline,
kwargs=dict(summary_labels=self.summary_labels))
# Baseline optimizer
if self.baseline_optimizer is None:
self.baseline_optimizer = None
else:
assert self.baseline_mode is not None
self.baseline_optimizer = Optimizer.from_spec(
spec=self.baseline_optimizer)
# TODO: Baseline internal states !!! (see target_network q_model)
# Reward estimation
self.fn_reward_estimation = tf.make_template(
name_=(self.scope + '/reward-estimation'),
func_=self.tf_reward_estimation,
custom_getter_=custom_getter)
# PG loss per instance function
self.fn_pg_loss_per_instance = tf.make_template(
name_=(self.scope + '/pg-loss-per-instance'),
func_=self.tf_pg_loss_per_instance,
custom_getter_=custom_getter)
class PGModel(DistributionModel):
"""
Base class for policy gradient models. It optionally defines a baseline
and handles its optimization. It implements the `tf_loss_per_instance` function, but requires
subclasses to implement `tf_pg_loss_per_instance`.
"""
def __init__(self, states_spec, actions_spec, network_spec, device,
session_config, scope, saver_spec, summary_spec,
distributed_spec, optimizer, discount, normalize_rewards,
variable_noise, distributions_spec, entropy_regularization,
baseline_mode, baseline, baseline_optimizer, gae_lambda):
# Baseline mode
assert baseline_mode is None or baseline_mode in ('states', 'network')
self.baseline_mode = baseline_mode
self.baseline = baseline
self.baseline_optimizer = baseline_optimizer
# Generalized advantage function
assert gae_lambda is None or (0.0 <= gae_lambda <= 1.0
and self.baseline_mode is not None)
self.gae_lambda = gae_lambda
super(PGModel, self).__init__(
states_spec=states_spec,
actions_spec=actions_spec,
network_spec=network_spec,
device=device,
session_config=session_config,
scope=scope,
saver_spec=saver_spec,
summary_spec=summary_spec,
distributed_spec=distributed_spec,
optimizer=optimizer,
discount=discount,
normalize_rewards=normalize_rewards,
variable_noise=variable_noise,
distributions_spec=distributions_spec,
entropy_regularization=entropy_regularization,
)
def initialize(self, custom_getter):
super(PGModel, self).initialize(custom_getter)
# Baseline
if self.baseline is None:
assert self.baseline_mode is None
self.baseline = None
elif all(name in self.states_spec for name in self.baseline):
# Implies AggregatedBaseline.
assert self.baseline_mode == 'states'
self.baseline = AggregatedBaseline(baselines=self.baseline)
else:
assert self.baseline_mode is not None
self.baseline = Baseline.from_spec(
spec=self.baseline,
kwargs=dict(summary_labels=self.summary_labels))
# Baseline optimizer
if self.baseline_optimizer is None:
self.baseline_optimizer = None
else:
assert self.baseline_mode is not None
self.baseline_optimizer = Optimizer.from_spec(
spec=self.baseline_optimizer)
# TODO: Baseline internal states !!! (see target_network q_model)
# Reward estimation
self.fn_reward_estimation = tf.make_template(
name_=(self.scope + '/reward-estimation'),
func_=self.tf_reward_estimation,
custom_getter_=custom_getter)
# PG loss per instance function
self.fn_pg_loss_per_instance = tf.make_template(
name_=(self.scope + '/pg-loss-per-instance'),
func_=self.tf_pg_loss_per_instance,
custom_getter_=custom_getter)
def tf_loss_per_instance(self, states, internals, actions, terminal,
reward, update):
reward = self.fn_reward_estimation(states=states,
internals=internals,
terminal=terminal,
reward=reward,
update=update)
return self.fn_pg_loss_per_instance(states=states,
internals=internals,
actions=actions,
terminal=terminal,
reward=reward,
update=update)
def tf_reward_estimation(self, states, internals, terminal, reward,
update):
if self.baseline_mode is None:
reward = self.fn_discounted_cumulative_reward(
terminal=terminal, reward=reward, discount=self.discount)
else:
if self.baseline_mode == 'states':
state_value = self.baseline.predict(states=states,
update=update)
elif self.baseline_mode == 'network':
embedding = self.network.apply(x=states,
internals=internals,
update=update)
state_value = self.baseline.predict(states=embedding,
update=update)
if self.gae_lambda is None:
reward = self.fn_discounted_cumulative_reward(
terminal=terminal, reward=reward, discount=self.discount)
reward -= state_value
else:
state_value_change = self.discount * state_value[
1:] - state_value[:-1]
state_value_change = tf.concat(values=(state_value_change,
(0.0, )),
axis=0)
zeros = tf.zeros_like(tensor=state_value_change)
state_value_change = tf.where(condition=terminal,
x=zeros,
y=state_value_change)
td_residual = reward + state_value_change
gae_discount = self.discount * self.gae_lambda
reward = self.fn_discounted_cumulative_reward(
terminal=terminal,
reward=td_residual,
discount=gae_discount)
return reward
def tf_pg_loss_per_instance(self, states, internals, actions, terminal,
reward, update):
"""
Creates the TensorFlow operations for calculating the (policy-gradient-specific) loss per batch
instance of the given input states and actions, after the specified reward/advantage calculations.
Args:
states: Dict of state tensors.
internals: List of prior internal state tensors.
actions: Dict of action tensors.
terminal: Terminal boolean tensor.
reward: Reward tensor.
update: Boolean tensor indicating whether this call happens during an update.
Returns:
Loss tensor.
"""
raise NotImplementedError
def tf_regularization_losses(self, states, internals, update):
losses = super(PGModel,
self).tf_regularization_losses(states=states,
internals=internals,
update=update)
if self.baseline_mode is not None and self.baseline_optimizer is None:
baseline_regularization_loss = self.baseline.regularization_loss()
if baseline_regularization_loss is not None:
losses['baseline'] = baseline_regularization_loss
return losses
def tf_optimization(self, states, internals, actions, terminal, reward,
update):
optimization = super(PGModel,
self).tf_optimization(states=states,
internals=internals,
actions=actions,
terminal=terminal,
reward=reward,
update=update)
if self.baseline_optimizer is None:
return optimization
reward = self.fn_discounted_cumulative_reward(terminal=terminal,
reward=reward,
discount=self.discount)
if self.baseline_mode == 'states':
def fn_loss():
loss = self.baseline.loss(states=states,
reward=reward,
update=update)
regularization_loss = self.baseline.regularization_loss()
if regularization_loss is None:
return loss
else:
return loss + regularization_loss
elif self.baseline_mode == 'network':
def fn_loss():
loss = self.baseline.loss(states=self.network.apply(
x=states, internals=internals, update=update),
reward=reward,
update=update)
regularization_loss = self.baseline.regularization_loss()
if regularization_loss is None:
return loss
else:
return loss + regularization_loss
# TODO: time as argument?
baseline_optimization = self.baseline_optimizer.minimize(
time=self.timestep,
variables=self.baseline.get_variables(),
fn_loss=fn_loss,
source_variables=self.network.get_variables())
return tf.group(optimization, baseline_optimization)
def get_variables(self, include_non_trainable=False):
model_variables = super(
PGModel,
self).get_variables(include_non_trainable=include_non_trainable)
if include_non_trainable and self.baseline_optimizer is not None:
# Baseline and optimizer variables included if 'include_non_trainable' set
baseline_variables = self.baseline.get_variables(
include_non_trainable=include_non_trainable)
baseline_optimizer_variables = self.baseline_optimizer.get_variables(
)
return model_variables + baseline_variables + baseline_optimizer_variables
elif self.baseline_mode is not None and self.baseline_optimizer is None:
# Baseline variables included if baseline_optimizer not set
baseline_variables = self.baseline.get_variables(
include_non_trainable=include_non_trainable)
return model_variables + baseline_variables
else:
return model_variables
def get_summaries(self):
if self.baseline_mode is None:
return super(PGModel, self).get_summaries()
else:
return super(PGModel,
self).get_summaries() + self.baseline.get_summaries()
class PGModel(DistributionModel):
"""
Base class for policy gradient models. It optionally defines a baseline
and handles its optimization. It implements the `tf_loss_per_instance` function, but requires
subclasses to implement `tf_pg_loss_per_instance`.
"""
def __init__(self, states, actions, scope, device, saver, summarizer,
distributed, batching_capacity, variable_noise,
states_preprocessing, actions_exploration,
reward_preprocessing, update_mode, memory, optimizer,
discount, network, distributions, entropy_regularization,
baseline_mode, baseline, baseline_optimizer, gae_lambda):
# Baseline mode
assert baseline_mode is None or baseline_mode in ('states', 'network')
self.baseline_mode = baseline_mode
self.baseline_spec = baseline
self.baseline_optimizer_spec = baseline_optimizer
# Generalized advantage function
assert gae_lambda is None or (0.0 <= gae_lambda <= 1.0
and self.baseline_mode is not None)
self.gae_lambda = gae_lambda
self.baseline = None
self.baseline_optimizer = None
self.fn_reward_estimation = None
super(PGModel,
self).__init__(states=states,
actions=actions,
scope=scope,
device=device,
saver=saver,
summarizer=summarizer,
distributed=distributed,
batching_capacity=batching_capacity,
variable_noise=variable_noise,
states_preprocessing=states_preprocessing,
actions_exploration=actions_exploration,
reward_preprocessing=reward_preprocessing,
update_mode=update_mode,
memory=memory,
optimizer=optimizer,
discount=discount,
network=network,
distributions=distributions,
entropy_regularization=entropy_regularization,
requires_deterministic=False)
def as_local_model(self):
super(PGModel, self).as_local_model()
if self.baseline_optimizer_spec is not None:
self.baseline_optimizer_spec = dict(
type='global_optimizer',
optimizer=self.baseline_optimizer_spec)
def initialize(self, custom_getter):
super(PGModel, self).initialize(custom_getter)
# Baseline
if self.baseline_spec is None:
assert self.baseline_mode is None
elif all(name in self.states_spec for name in self.baseline_spec):
# Implies AggregatedBaseline.
assert self.baseline_mode == 'states'
self.baseline = AggregatedBaseline(baselines=self.baseline_spec)
else:
assert self.baseline_mode is not None
self.baseline = Baseline.from_spec(
spec=self.baseline_spec,
kwargs=dict(summary_labels=self.summary_labels))
# Baseline optimizer
if self.baseline_optimizer_spec is not None:
assert self.baseline_mode is not None
self.baseline_optimizer = Optimizer.from_spec(
spec=self.baseline_optimizer_spec)
# TODO: Baseline internal states !!! (see target_network q_model)
# Reward estimation
self.fn_reward_estimation = tf.make_template(
name_='reward-estimation',
func_=self.tf_reward_estimation,
custom_getter_=custom_getter)
# Baseline loss
self.fn_baseline_loss = tf.make_template(name_='baseline-loss',
func_=self.tf_baseline_loss,
custom_getter_=custom_getter)
def tf_reward_estimation(self, states, internals, terminal, reward,
update):
if self.baseline_mode is None:
return self.fn_discounted_cumulative_reward(terminal=terminal,
reward=reward,
discount=self.discount)
else:
if self.baseline_mode == 'states':
state_value = self.baseline.predict(states=states,
internals=internals,
update=update)
elif self.baseline_mode == 'network':
embedding = self.network.apply(x=states,
internals=internals,
update=update)
state_value = self.baseline.predict(
states=tf.stop_gradient(input=embedding),
internals=internals,
update=update)
if self.gae_lambda is None:
reward = self.fn_discounted_cumulative_reward(
terminal=terminal, reward=reward, discount=self.discount)
advantage = reward - state_value
else:
next_state_value = tf.concat(values=(state_value[1:], (0.0, )),
axis=0)
zeros = tf.zeros_like(tensor=next_state_value)
next_state_value = tf.where(condition=terminal,
x=zeros,
y=next_state_value)
td_residual = reward + self.discount * next_state_value - state_value
gae_discount = self.discount * self.gae_lambda
advantage = self.fn_discounted_cumulative_reward(
terminal=terminal,
reward=td_residual,
discount=gae_discount)
# Normalize advantage.
# mean, variance = tf.nn.moments(advantage, axes=[0], keep_dims=True)
# advantage = (advantage - mean) / tf.sqrt(x=tf.maximum(x=variance, y=util.epsilon))
return advantage
def tf_regularization_losses(self, states, internals, update):
losses = super(PGModel,
self).tf_regularization_losses(states=states,
internals=internals,
update=update)
if self.baseline_mode is not None and self.baseline_optimizer is None:
baseline_regularization_loss = self.baseline.regularization_loss()
if baseline_regularization_loss is not None:
losses['baseline'] = baseline_regularization_loss
return losses
def tf_baseline_loss(self,
states,
internals,
reward,
update,
reference=None):
"""
Creates the TensorFlow operations for calculating the baseline loss of a batch.
Args:
states: Dict of state tensors.
internals: List of prior internal state tensors.
reward: Reward tensor.
update: Boolean tensor indicating whether this call happens during an update.
reference: Optional reference tensor(s), in case of a comparative loss.
Returns:
Loss tensor.
"""
if self.baseline_mode == 'states':
loss = self.baseline.loss(states=states,
internals=internals,
reward=reward,
update=update,
reference=reference)
elif self.baseline_mode == 'network':
loss = self.baseline.loss(states=self.network.apply(
x=states, internals=internals, update=update),
internals=internals,
reward=reward,
update=update,
reference=reference)
regularization_loss = self.baseline.regularization_loss()
if regularization_loss is not None:
loss += regularization_loss
return loss
def baseline_optimizer_arguments(self, states, internals, reward):
"""
Returns the baseline optimizer arguments including the time, the list of variables to
optimize, and various functions which the optimizer might require to perform an update
step.
Args:
states: Dict of state tensors.
internals: List of prior internal state tensors.
reward: Reward tensor.
Returns:
Baseline optimizer arguments as dict.
"""
arguments = dict(
time=self.global_timestep,
variables=self.baseline.get_variables(),
arguments=dict(
states=states,
internals=internals,
reward=reward,
update=tf.constant(value=True),
),
fn_reference=self.baseline.reference,
fn_loss=self.fn_baseline_loss,
# source_variables=self.network.get_variables()
)
if self.global_model is not None:
arguments[
'global_variables'] = self.global_model.baseline.get_variables(
)
return arguments
def tf_optimization(self,
states,
internals,
actions,
terminal,
reward,
next_states=None,
next_internals=None):
assert next_states is None and next_internals is None # temporary
estimated_reward = self.fn_reward_estimation(
states=states,
internals=internals,
terminal=terminal,
reward=reward,
update=tf.constant(value=True))
if self.baseline_optimizer is not None:
estimated_reward = tf.stop_gradient(input=estimated_reward)
optimization = super(PGModel, self).tf_optimization(
states=states,
internals=internals,
actions=actions,
terminal=terminal,
reward=estimated_reward,
next_states=next_states,
next_internals=next_internals)
if self.baseline_optimizer is not None:
cumulative_reward = self.fn_discounted_cumulative_reward(
terminal=terminal, reward=reward, discount=self.discount)
arguments = self.baseline_optimizer_arguments(
states=states,
internals=internals,
reward=cumulative_reward,
)
baseline_optimization = self.baseline_optimizer.minimize(
**arguments)
optimization = tf.group(optimization, baseline_optimization)
return optimization
def get_variables(self,
include_submodules=False,
include_nontrainable=False):
model_variables = super(PGModel, self).get_variables(
include_submodules=include_submodules,
include_nontrainable=include_nontrainable)
if self.baseline_mode is not None and (
include_submodules or self.baseline_optimizer is None):
baseline_variables = self.baseline.get_variables(
include_nontrainable=include_nontrainable)
model_variables += baseline_variables
if include_nontrainable and self.baseline_optimizer is not None:
baseline_optimizer_variables = self.baseline_optimizer.get_variables(
)
# For some reason, some optimizer variables are only registered in the model.
for variable in baseline_optimizer_variables:
if variable in model_variables:
model_variables.remove(variable)
model_variables += baseline_optimizer_variables
return model_variables
def get_summaries(self):
if self.baseline_mode is None:
return super(PGModel, self).get_summaries()
else:
return super(PGModel,
self).get_summaries() + self.baseline.get_summaries()
class PGModel(DistributionModel):
"""Base class for policy gradient models."""
def __init__(self, states_spec, actions_spec, network_spec, config):
# Baseline mode
assert config.baseline_mode is None or config.baseline_mode in (
'states', 'network')
self.baseline_mode = config.baseline_mode
with tf.name_scope(name=config.scope):
# Baseline
if config.baseline is None:
assert self.baseline_mode is None
self.baseline = None
elif all(name in states_spec for name in config.baseline):
# Implies AggregatedBaseline
assert self.baseline_mode == 'states'
self.baseline = AggregatedBaseline(baselines=config.baseline)
else:
assert self.baseline_mode is not None
self.baseline = Baseline.from_spec(
spec=config.baseline,
kwargs=dict(summary_labels=config.summary_labels))
# Baseline optimizer
if config.baseline_optimizer is None:
self.baseline_optimizer = None
else:
assert self.baseline_mode is not None
self.baseline_optimizer = Optimizer.from_spec(
spec=config.baseline_optimizer)
# Generalized advantage function
assert config.gae_lambda is None or (
0.0 <= config.gae_lambda <= 1.0 and self.baseline_mode is not None)
self.gae_lambda = config.gae_lambda
super(PGModel, self).__init__(states_spec=states_spec,
actions_spec=actions_spec,
network_spec=network_spec,
config=config)
def initialize(self, custom_getter):
super(PGModel, self).initialize(custom_getter)
# TODO: Baseline internal states !!! (see target_network q_model)
# PG loss per instance function
self.fn_pg_loss_per_instance = tf.make_template(
name_='pg-loss-per-instance',
func_=self.tf_pg_loss_per_instance,
custom_getter_=custom_getter)
def tf_pg_loss_per_instance(self, states, internals, actions, terminal,
reward):
"""
Creates the TensorFlow operations for calculating the (policy-gradient-specific) loss per batch
instance of the given input states and actions, after the specified reward/advantage calculations.
Args:
states: Dict of state tensors.
internals: List of prior internal state tensors.
actions: Dict of action tensors.
terminal: Terminal boolean tensor.
reward: Reward tensor.
Returns:
Loss tensor.
"""
raise NotImplementedError
def tf_loss_per_instance(self, states, internals, actions, terminal,
reward):
if self.baseline_mode is None:
reward = self.fn_discounted_cumulative_reward(
terminal=terminal, reward=reward, discount=self.discount)
else:
if self.baseline_mode == 'states':
state_value = self.baseline.predict(states=states)
elif self.baseline_mode == 'network':
embedding = self.network.apply(x=states, internals=internals)
state_value = self.baseline.predict(states=embedding)
if self.gae_lambda is None:
reward = self.fn_discounted_cumulative_reward(
terminal=terminal, reward=reward, discount=self.discount)
reward -= state_value
else:
state_value_change = self.discount * state_value[
1:] - state_value[:-1]
state_value_change = tf.concat(values=(state_value_change,
(0.0, )),
axis=0)
zeros = tf.zeros_like(tensor=state_value_change)
state_value_change = tf.where(condition=terminal,
x=zeros,
y=state_value_change)
td_residual = reward + state_value_change
gae_discount = self.discount * self.gae_lambda
reward = self.fn_discounted_cumulative_reward(
terminal=terminal,
reward=td_residual,
discount=gae_discount)
return self.fn_pg_loss_per_instance(states=states,
internals=internals,
actions=actions,
terminal=terminal,
reward=reward)
def tf_regularization_losses(self, states, internals):
losses = super(PGModel,
self).tf_regularization_losses(states=states,
internals=internals)
if self.baseline_mode is not None and \
self.baseline_optimizer is None and \
self.baseline.regularization_loss() is not None:
losses['baseline'] = self.baseline.regularization_loss()
return losses
def tf_optimization(self, states, internals, actions, terminal, reward):
optimization = super(PGModel,
self).tf_optimization(states, internals, actions,
terminal, reward)
if self.baseline_mode is None:
return optimization
reward = self.fn_discounted_cumulative_reward(terminal=terminal,
reward=reward,
discount=self.discount)
if self.baseline_mode == 'states':
fn_loss = (
lambda: self.baseline.loss(states=states, reward=reward))
elif self.baseline_mode == 'network':
fn_loss = (lambda: self.baseline.loss(states=self.network.apply(
x=states, internals=internals),
reward=reward))
# TODO: time as argument?
baseline_optimization = self.baseline_optimizer.minimize(
time=self.timestep,
variables=self.baseline.get_variables(),
fn_loss=fn_loss,
source_variables=self.network.get_variables())
return tf.group(optimization, baseline_optimization)
def get_variables(self, include_non_trainable=False):
model_variables = super(
PGModel,
self).get_variables(include_non_trainable=include_non_trainable)
if include_non_trainable and self.baseline_mode is not None:
# Baseline and optimizer variables included if 'include_non_trainable' set
baseline_variables = self.baseline.get_variables(
include_non_trainable=include_non_trainable)
baseline_optimizer_variables = self.baseline_optimizer.get_variables(
)
return model_variables + baseline_variables + baseline_optimizer_variables
elif self.baseline_mode is not None and self.baseline_optimizer is None:
# Baseline variables included if baseline_optimizer not set
baseline_variables = self.baseline.get_variables(
include_non_trainable=include_non_trainable)
return model_variables + baseline_variables
else:
return model_variables
def get_summaries(self):
if self.baseline_mode is None:
return super(PGModel, self).get_summaries()
else:
return super(PGModel,
self).get_summaries() + self.baseline.get_summaries()
