def add_policy(self, parsed_behavior_id: BehaviorIdentifiers,
policy: TFPolicy) -> None:
"""
Adds policy to trainer. The first policy encountered sets the wrapped
trainer team. This is to ensure that all agents from the same multi-agent
team are grouped. All policies associated with this team are added to the
wrapped trainer to be trained.
:param name_behavior_id: Behavior ID that the policy should belong to.
:param policy: Policy to associate with name_behavior_id.
"""
name_behavior_id = parsed_behavior_id.behavior_id
team_id = parsed_behavior_id.team_id
self.controller.subscribe_team_id(team_id, self)
self.policies[name_behavior_id] = policy
policy.create_tf_graph()
self._name_to_parsed_behavior_id[name_behavior_id] = parsed_behavior_id
# for saving/swapping snapshots
policy.init_load_weights()
# First policy or a new agent on the same team encountered
if self.wrapped_trainer_team is None or team_id == self.wrapped_trainer_team:
self.current_policy_snapshot[
parsed_behavior_id.brain_name] = policy.get_weights()
self._save_snapshot(
) # Need to save after trainer initializes policy
self.trainer.add_policy(parsed_behavior_id, policy)
self._learning_team = self.controller.get_learning_team
self.wrapped_trainer_team = team_id
def add_policy(self, name_behavior_id: str, policy: TFPolicy) -> None:
"""
Adds policy to trainer. For the first policy added, add a trainer
to the policy and set the learning behavior name to name_behavior_id.
:param name_behavior_id: Behavior ID that the policy should belong to.
:param policy: Policy to associate with name_behavior_id.
"""
self.policies[name_behavior_id] = policy
policy.create_tf_graph()
# First policy encountered
if not self.learning_behavior_name:
weights = policy.get_weights()
self.current_policy_snapshot = weights
self.trainer.add_policy(name_behavior_id, policy)
self._save_snapshot(
policy) # Need to save after trainer initializes policy
self.learning_behavior_name = name_behavior_id
behavior_id_parsed = BehaviorIdentifiers.from_name_behavior_id(
self.learning_behavior_name)
team_id = behavior_id_parsed.behavior_ids["team"]
self._stats_reporter.add_property(StatsPropertyType.SELF_PLAY_TEAM,
team_id)
else:
# for saving/swapping snapshots
policy.init_load_weights()
def add_policy(self, name_behavior_id: str, policy: TFPolicy) -> None:
"""
Adds policy to trainer. For the first policy added, add a trainer
to the policy and set the learning behavior name to name_behavior_id.
:param name_behavior_id: Behavior ID that the policy should belong to.
:param policy: Policy to associate with name_behavior_id.
"""
self.policies[name_behavior_id] = policy
policy.create_tf_graph()
# First policy encountered
if not self.learning_behavior_name:
weights = policy.get_weights()
self.current_policy_snapshot = weights
self.trainer.add_policy(name_behavior_id, policy)
self._save_snapshot(policy) # Need to save after trainer initializes policy
self.learning_behavior_name = name_behavior_id
else:
# for saving/swapping snapshots
policy.init_load_weights()
def __init__(self, policy: TFPolicy, trainer_params: Dict[str, Any]):
"""
Takes a Policy and a Dict of trainer parameters and creates an Optimizer around the policy.
The PPO optimizer has a value estimator and a loss function.
:param policy: A TFPolicy object that will be updated by this PPO Optimizer.
:param trainer_params: Trainer parameters dictionary that specifies the properties of the trainer.
"""
# Create the graph here to give more granular control of the TF graph to the Optimizer.
policy.create_tf_graph()
with policy.graph.as_default():
with tf.variable_scope("optimizer/"):
super().__init__(policy, trainer_params)
lr = float(trainer_params["learning_rate"])
lr_schedule = LearningRateSchedule(
trainer_params.get("learning_rate_schedule", "linear"))
h_size = int(trainer_params["hidden_units"])
epsilon = float(trainer_params["epsilon"])
beta = float(trainer_params["beta"])
max_step = float(trainer_params["max_steps"])
num_layers = int(trainer_params["num_layers"])
vis_encode_type = EncoderType(
trainer_params.get("vis_encode_type", "simple"))
self.burn_in_ratio = float(
trainer_params.get("burn_in_ratio", 0.0))
self.stream_names = list(self.reward_signals.keys())
self.tf_optimizer: Optional[tf.train.AdamOptimizer] = None
self.grads = None
self.update_batch: Optional[tf.Operation] = None
self.stats_name_to_update_name = {
"Losses/Value Loss": "value_loss",
"Losses/Policy Loss": "policy_loss",
"Policy/Learning Rate": "learning_rate",
}
if self.policy.use_recurrent:
self.m_size = self.policy.m_size
self.memory_in = tf.placeholder(
shape=[None, self.m_size],
dtype=tf.float32,
name="recurrent_value_in",
)
if num_layers < 1:
num_layers = 1
if policy.use_continuous_act:
self._create_cc_critic(h_size, num_layers, vis_encode_type)
else:
self._create_dc_critic(h_size, num_layers, vis_encode_type)
self.learning_rate = ModelUtils.create_learning_rate(
lr_schedule, lr, self.policy.global_step, int(max_step))
self._create_losses(
self.policy.total_log_probs,
self.old_log_probs,
self.value_heads,
self.policy.entropy,
beta,
epsilon,
lr,
max_step,
)
self._create_ppo_optimizer_ops()
self.update_dict.update({
"value_loss": self.value_loss,
"policy_loss": self.abs_policy_loss,
"update_batch": self.update_batch,
"learning_rate": self.learning_rate,
})
self.policy.initialize_or_load()
def __init__(self, policy: TFPolicy, trainer_params: TrainerSettings):
"""
Takes a Unity environment and model-specific hyper-parameters and returns the
appropriate PPO agent model for the environment.
:param brain: Brain parameters used to generate specific network graph.
:param lr: Learning rate.
:param lr_schedule: Learning rate decay schedule.
:param h_size: Size of hidden layers
:param init_entcoef: Initial value for entropy coefficient. Set lower to learn faster,
set higher to explore more.
:return: a sub-class of PPOAgent tailored to the environment.
:param max_step: Total number of training steps.
:param normalize: Whether to normalize vector observation input.
:param use_recurrent: Whether to use an LSTM layer in the network.
:param num_layers: Number of hidden layers between encoded input and policy & value layers
:param tau: Strength of soft-Q update.
:param m_size: Size of brain memory.
"""
# Create the graph here to give more granular control of the TF graph to the Optimizer.
policy.create_tf_graph()
with policy.graph.as_default():
with tf.variable_scope(""):
super().__init__(policy, trainer_params)
hyperparameters: SACSettings = cast(
SACSettings, trainer_params.hyperparameters)
lr = hyperparameters.learning_rate
lr_schedule = hyperparameters.learning_rate_schedule
max_step = trainer_params.max_steps
self.tau = hyperparameters.tau
self.init_entcoef = hyperparameters.init_entcoef
self.policy = policy
self.act_size = policy.act_size
policy_network_settings = policy.network_settings
h_size = policy_network_settings.hidden_units
num_layers = policy_network_settings.num_layers
vis_encode_type = policy_network_settings.vis_encode_type
self.tau = hyperparameters.tau
self.burn_in_ratio = 0.0
# Non-exposed SAC parameters
self.discrete_target_entropy_scale = (
0.2 # Roughly equal to e-greedy 0.05
)
self.continuous_target_entropy_scale = 1.0
stream_names = list(self.reward_signals.keys())
# Use to reduce "survivor bonus" when using Curiosity or GAIL.
self.gammas = [
_val.gamma
for _val in trainer_params.reward_signals.values()
]
self.use_dones_in_backup = {
name: tf.Variable(1.0)
for name in stream_names
}
self.disable_use_dones = {
name: self.use_dones_in_backup[name].assign(0.0)
for name in stream_names
}
if num_layers < 1:
num_layers = 1
self.target_init_op: List[tf.Tensor] = []
self.target_update_op: List[tf.Tensor] = []
self.update_batch_policy: Optional[tf.Operation] = None
self.update_batch_value: Optional[tf.Operation] = None
self.update_batch_entropy: Optional[tf.Operation] = None
self.policy_network = SACPolicyNetwork(
policy=self.policy,
m_size=self.policy.m_size, # 3x policy.m_size
h_size=h_size,
normalize=self.policy.normalize,
use_recurrent=self.policy.use_recurrent,
num_layers=num_layers,
stream_names=stream_names,
vis_encode_type=vis_encode_type,
)
self.target_network = SACTargetNetwork(
policy=self.policy,
m_size=self.policy.m_size, # 1x policy.m_size
h_size=h_size,
normalize=self.policy.normalize,
use_recurrent=self.policy.use_recurrent,
num_layers=num_layers,
stream_names=stream_names,
vis_encode_type=vis_encode_type,
)
# The optimizer's m_size is 3 times the policy (Q1, Q2, and Value)
self.m_size = 3 * self.policy.m_size
self._create_inputs_and_outputs()
self.learning_rate = ModelUtils.create_schedule(
lr_schedule,
lr,
self.policy.global_step,
int(max_step),
min_value=1e-10,
)
self._create_losses(
self.policy_network.q1_heads,
self.policy_network.q2_heads,
lr,
int(max_step),
stream_names,
discrete=not self.policy.use_continuous_act,
)
self._create_sac_optimizer_ops()
self.selected_actions = (self.policy.selected_actions
) # For GAIL and other reward signals
if self.policy.normalize:
target_update_norm = self.target_network.copy_normalization(
self.policy.running_mean,
self.policy.running_variance,
self.policy.normalization_steps,
)
# Update the normalization of the optimizer when the policy does.
self.policy.update_normalization_op = tf.group([
self.policy.update_normalization_op, target_update_norm
])
self.stats_name_to_update_name = {
"Losses/Value Loss": "value_loss",
"Losses/Policy Loss": "policy_loss",
"Losses/Q1 Loss": "q1_loss",
"Losses/Q2 Loss": "q2_loss",
"Policy/Entropy Coeff": "entropy_coef",
"Policy/Learning Rate": "learning_rate",
}
self.update_dict = {
"value_loss": self.total_value_loss,
"policy_loss": self.policy_loss,
"q1_loss": self.q1_loss,
"q2_loss": self.q2_loss,
"entropy_coef": self.ent_coef,
"update_batch": self.update_batch_policy,
"update_value": self.update_batch_value,
"update_entropy": self.update_batch_entropy,
"learning_rate": self.learning_rate,
}
def __init__(self, policy: TFPolicy, trainer_params: TrainerSettings):
"""
Takes a Policy and a Dict of trainer parameters and creates an Optimizer around the policy.
The PPO optimizer has a value estimator and a loss function.
:param policy: A TFPolicy object that will be updated by this PPO Optimizer.
:param trainer_params: Trainer parameters dictionary that specifies the properties of the trainer.
"""
# Create the graph here to give more granular control of the TF graph to the Optimizer.
policy.create_tf_graph()
with policy.graph.as_default():
with tf.variable_scope("optimizer/"):
super().__init__(policy, trainer_params)
hyperparameters: PPOSettings = cast(
PPOSettings, trainer_params.hyperparameters)
lr = float(hyperparameters.learning_rate)
self._schedule = hyperparameters.learning_rate_schedule
epsilon = float(hyperparameters.epsilon)
beta = float(hyperparameters.beta)
max_step = float(trainer_params.max_steps)
policy_network_settings = policy.network_settings
h_size = int(policy_network_settings.hidden_units)
num_layers = policy_network_settings.num_layers
vis_encode_type = policy_network_settings.vis_encode_type
self.burn_in_ratio = 0.0
self.stream_names = list(self.reward_signals.keys())
self.tf_optimizer_op: Optional[tf.train.Optimizer] = None
self.grads = None
self.update_batch: Optional[tf.Operation] = None
self.stats_name_to_update_name = {
"Losses/Value Loss": "value_loss",
"Losses/Policy Loss": "policy_loss",
"Policy/Learning Rate": "learning_rate",
"Policy/Epsilon": "decay_epsilon",
"Policy/Beta": "decay_beta",
}
if self.policy.use_recurrent:
self.m_size = self.policy.m_size
self.memory_in = tf.placeholder(
shape=[None, self.m_size],
dtype=tf.float32,
name="recurrent_value_in",
)
if num_layers < 1:
num_layers = 1
if policy.use_continuous_act:
self._create_cc_critic(h_size, num_layers, vis_encode_type)
else:
self._create_dc_critic(h_size, num_layers, vis_encode_type)
self.learning_rate = ModelUtils.create_schedule(
self._schedule,
lr,
self.policy.global_step,
int(max_step),
min_value=1e-10,
)
self._create_losses(
self.policy.total_log_probs,
self.old_log_probs,
self.value_heads,
self.policy.entropy,
beta,
epsilon,
lr,
max_step,
)
self._create_ppo_optimizer_ops()
self.update_dict.update({
"value_loss": self.value_loss,
"policy_loss": self.abs_policy_loss,
"update_batch": self.update_batch,
"learning_rate": self.learning_rate,
"decay_epsilon": self.decay_epsilon,
"decay_beta": self.decay_beta,
})