def __init__(
self,
policy: TFPolicy,
strength: float,
gamma: float,
encoding_size: int = 128,
learning_rate: float = 3e-4,
num_epoch: int = 3,
):
"""
Creates the Curiosity reward generator
:param policy: The Learning Policy
:param strength: The scaling parameter for the reward. The scaled reward will be the unscaled
reward multiplied by the strength parameter
:param gamma: The time discounting factor used for this reward.
:param encoding_size: The size of the hidden encoding layer for the ICM
:param learning_rate: The learning rate for the ICM.
:param num_epoch: The number of epochs to train over the training buffer for the ICM.
"""
super().__init__(policy, strength, gamma)
self.model = CuriosityModel(
policy.model, encoding_size=encoding_size, learning_rate=learning_rate
)
self.num_epoch = num_epoch
self.use_terminal_states = False
self.update_dict = {
"forward_loss": self.model.forward_loss,
"inverse_loss": self.model.inverse_loss,
"update": self.model.update_batch,
}
self.has_updated = False
def __init__(
self,
policy: TFPolicy,
strength: float,
gamma: float,
encoding_size: int = 128,
learning_rate: float = 3e-4,
):
"""
Creates the Curiosity reward generator
:param policy: The Learning Policy
:param strength: The scaling parameter for the reward. The scaled reward will be the unscaled
reward multiplied by the strength parameter
:param gamma: The time discounting factor used for this reward.
:param encoding_size: The size of the hidden encoding layer for the ICM
:param learning_rate: The learning rate for the ICM.
"""
super().__init__(policy, strength, gamma)
self.model = CuriosityModel(policy,
encoding_size=encoding_size,
learning_rate=learning_rate)
self.use_terminal_states = False
self.update_dict = {
"curiosity_forward_loss": self.model.forward_loss,
"curiosity_inverse_loss": self.model.inverse_loss,
"curiosity_update": self.model.update_batch,
}
self.stats_name_to_update_name = {
"Losses/Curiosity Forward Loss": "curiosity_forward_loss",
"Losses/Curiosity Inverse Loss": "curiosity_inverse_loss",
}
self.has_updated = False
def __init__(self, policy: TFPolicy, settings: CuriositySettings):
"""
Creates the Curiosity reward generator
:param policy: The Learning Policy
:param settings: CuriositySettings object that contains the parameters
(including encoding size and learning rate) for this CuriosityRewardSignal.
"""
super().__init__(policy, settings)
self.model = CuriosityModel(
policy,
encoding_size=settings.encoding_size,
learning_rate=settings.learning_rate,
)
self.use_terminal_states = False
self.update_dict = {
"curiosity_forward_loss": self.model.forward_loss,
"curiosity_inverse_loss": self.model.inverse_loss,
"curiosity_update": self.model.update_batch,
}
self.stats_name_to_update_name = {
"Losses/Curiosity Forward Loss": "curiosity_forward_loss",
"Losses/Curiosity Inverse Loss": "curiosity_inverse_loss",
}
self.has_updated = False