def create_distributions(self):
distributions = dict()
for name, action in self.actions_spec.items():
if self.distributions_spec is not None and name in self.distributions_spec:
kwargs = dict(action)
kwargs['summary_labels'] = self.summary_labels
distributions[name] = Distribution.from_spec(
spec=self.distributions_spec[name], kwargs=kwargs)
elif action['type'] == 'bool':
distributions[name] = Bernoulli(
shape=action['shape'], summary_labels=self.summary_labels)
elif action['type'] == 'int':
distributions[name] = Categorical(
shape=action['shape'],
num_actions=action['num_actions'],
summary_labels=self.summary_labels)
elif action['type'] == 'float':
if 'min_value' in action:
distributions[name] = Beta(
shape=action['shape'],
min_value=action['min_value'],
max_value=action['max_value'],
summary_labels=self.summary_labels)
else:
distributions[name] = Gaussian(
shape=action['shape'],
summary_labels=self.summary_labels)
return distributions
def __init__(self, config):
config.default(PolicyGradientModel.default_config)
# distribution
self.distribution = dict()
for name, action in config.actions:
if 'distribution' in action:
kwargs = dict(action)
self.distribution[name] = Distribution.from_config(
config=action.distribution, kwargs=kwargs)
elif action.continuous:
if action.min_value is None:
assert action.max_value is None
self.distribution[name] = Gaussian(shape=action.shape)
else:
assert action.max_value is not None
self.distribution[name] = Beta(min_value=action.min_value,
max_value=action.max_value,
shape=action.shape)
else:
self.distribution[name] = Categorical(
shape=action.shape, num_actions=action.num_actions)
# baseline
if config.baseline is None:
self.baseline = None
else:
self.baseline = Baseline.from_config(config=config.baseline)
# advantage estimation
self.gae_rewards = config.gae_rewards
self.gae_lambda = config.gae_lambda
self.normalize_rewards = config.normalize_rewards
super(PolicyGradientModel, self).__init__(config)
def __init__(self, config):
config.default(PolicyGradientModel.default_config)
# distribution
self.distribution = dict()
for name, action in config.actions:
if 'distribution' in action:
if not action.continuous:
kwargs = dict(num_actions=action.num_actions)
elif 'min_value' in action:
kwargs = dict(min_value=action.min_value, max_value=action.max_value)
else:
kwargs = dict()
self.distribution[name] = Distribution.from_config(config=action.distribution, kwargs=kwargs)
# elif 'min_value' in action:
# ...
elif action.continuous:
self.distribution[name] = Gaussian()
else:
self.distribution[name] = Categorical(num_actions=action.num_actions)
# baseline
if config.baseline is None:
self.baseline = None
else:
self.baseline = Baseline.from_config(config=config.baseline)
super(PolicyGradientModel, self).__init__(config)
# advantage estimation
self.generalized_advantage_estimation = config.generalized_advantage_estimation
if self.generalized_advantage_estimation:
self.gae_lambda = config.gae_lambda
self.normalize_advantage = config.normalize_advantage
def __init__(self, states_spec, actions_spec, network_spec, config):
with tf.name_scope(name=config.scope):
# Network
self.network = Network.from_spec(
spec=network_spec,
kwargs=dict(summary_labels=config.summary_labels))
# Distributions
self.distributions = dict()
for name, action in actions_spec.items():
with tf.name_scope(name=(name + '-distribution')):
if config.distributions is not None and name in config.distributions:
kwargs = dict(action)
kwargs['summary_labels'] = config.summary_labels
self.distributions[name] = Distribution.from_spec(
spec=config.distributions[name], kwargs=kwargs)
elif action['type'] == 'bool':
self.distributions[name] = Bernoulli(
shape=action['shape'],
summary_labels=config.summary_labels)
elif action['type'] == 'int':
self.distributions[name] = Categorical(
shape=action['shape'],
num_actions=action['num_actions'],
summary_labels=config.summary_labels)
elif action['type'] == 'float':
if 'min_value' in action:
self.distributions[name] = Beta(
shape=action['shape'],
min_value=action['min_value'],
max_value=action['max_value'],
summary_labels=config.summary_labels)
else:
self.distributions[name] = Gaussian(
shape=action['shape'],
summary_labels=config.summary_labels)
# Entropy regularization
assert config.entropy_regularization is None or config.entropy_regularization >= 0.0
self.entropy_regularization = config.entropy_regularization
super(DistributionModel, self).__init__(states_spec=states_spec,
actions_spec=actions_spec,
network_spec=network_spec,
config=config)
def initialize(self, custom_getter):
super(DistributionModel, self).initialize(custom_getter)
# Network
self.network = Network.from_spec(
spec=self.network_spec,
kwargs=dict(summary_labels=self.summary_labels))
# Distributions
self.distributions = dict()
for name, action in self.actions_spec.items():
with tf.name_scope(name=(name + '-distribution')):
if self.distributions_spec is not None and name in self.distributions_spec:
kwargs = dict(action)
kwargs['summary_labels'] = self.summary_labels
self.distributions[name] = Distribution.from_spec(
spec=self.distributions_spec[name], kwargs=kwargs)
elif action['type'] == 'bool':
self.distributions[name] = Bernoulli(
shape=action['shape'],
summary_labels=self.summary_labels)
elif action['type'] == 'int':
self.distributions[name] = Categorical(
shape=action['shape'],
num_actions=action['num_actions'],
summary_labels=self.summary_labels)
elif action['type'] == 'float':
if 'min_value' in action:
self.distributions[name] = Beta(
shape=action['shape'],
min_value=action['min_value'],
max_value=action['max_value'],
summary_labels=self.summary_labels)
else:
self.distributions[name] = Gaussian(
shape=action['shape'],
summary_labels=self.summary_labels)
# Network internals
self.internal_inputs.extend(self.network.internal_inputs())
self.internal_inits.extend(self.network.internal_inits())
# KL divergence function
self.fn_kl_divergence = tf.make_template(name_='kl-divergence',
func_=self.tf_kl_divergence,
custom_getter_=custom_getter)
def create_distributions(self):
"""
Creates and returns the Distribution objects based on self.distributions_spec.
Returns: Dict of distributions according to self.distributions_spec.
"""
distributions = dict()
for name in sorted(self.actions_spec):
action = self.actions_spec[name]
if self.distributions_spec is not None and name in self.distributions_spec:
kwargs = dict(action)
kwargs['scope'] = name
kwargs['summary_labels'] = self.summary_labels
distributions[name] = Distribution.from_spec(
spec=self.distributions_spec[name], kwargs=kwargs)
elif action['type'] == 'bool':
distributions[name] = Bernoulli(
shape=action['shape'],
scope=name,
summary_labels=self.summary_labels)
elif action['type'] == 'int':
distributions[name] = Categorical(
shape=action['shape'],
num_actions=action['num_actions'],
scope=name,
summary_labels=self.summary_labels)
elif action['type'] == 'float':
if 'min_value' in action:
distributions[name] = Beta(
shape=action['shape'],
min_value=action['min_value'],
max_value=action['max_value'],
scope=name,
summary_labels=self.summary_labels)
else:
distributions[name] = Gaussian(
shape=action['shape'],
scope=name,
summary_labels=self.summary_labels)
return distributions
def generate_distributions(actions_spec, distributions_spec,
summary_labels):
distributions = dict()
for name, action in actions_spec.items():
with tf.name_scope(name=(name + '-distribution')):
if distributions_spec is not None and name in distributions_spec:
kwargs = dict(action)
kwargs['summary_labels'] = summary_labels
distributions[name] = Distribution.from_spec(
spec=distributions_spec[name], kwargs=kwargs)
elif action['type'] == 'bool':
distributions[name] = Bernoulli(
shape=action['shape'], summary_labels=summary_labels)
elif action['type'] == 'int':
distributions[name] = Categorical(
shape=action['shape'],
num_actions=action['num_actions'],
summary_labels=summary_labels)
elif action['type'] == 'float':
if 'min_value' in action:
distributions[name] = Beta(
shape=action['shape'],
min_value=action['min_value'],
max_value=action['max_value'],
summary_labels=summary_labels)
else:
distributions[name] = Gaussian(
shape=action['shape'],
summary_labels=summary_labels)
return distributions