EscalationRule: EscalationRule Class
In RLescalation: Optimal Dose Escalation Using Deep Reinforcement Learning
EscalationRule R Documentation
EscalationRule Class
Description
This class represents an escalation rule that generates a next escalation.
Public fields
policyThe RLlib policy that is a Python object.
dirDirectory path of the escalation rule (policy).
dirpathFull path to the directory of the escalation rule.
created_atCreated time of this object.
infoInformation when learning the escalation rule.
inputInputs for learning the escalation rule.
logThe log of scores during the learning of the escalation rule.
checkpointsThe integer vector of iteration counts for checkpoints.
checkpoints_pathsThe paths to the directories where each checkpoint is stored.
Methods
Public methods
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Method new()
Create a new EscalationRule object.
Usage
EscalationRule$new(dir = "latest", base_dir = "escalation_rules")
Arguments
dirA character value. A directory name or path where an
escalation rule is outputted. By default, the latest escalation
rule is searched in 'base_dir'.
base_dirA character value. A directory path that is used as the
parent directory if the 'dir' argument is a directory name and is
not used otherwise.
Method opt_action()
Compute optimal action probabilities using the obtained escalation rule
for data of N and DLT.
Usage
EscalationRule$opt_action(current_dose, data_Ns, data_DLTs)
Arguments
current_doseAn integer value. This is the current dose index,
which is within 1:J.
data_NsA numeric vector. The cumulative number of patients
assigned to each dose in your clinical trial.
data_DLTsA numeric vector. The cumulative number of DLTs
corresponding to each dose for the 'data_Ns' argument.
Returns
A character that represents the optimal action. One of the followings:
down, stay, up, MTD_1, ..., MTD_J, no_MTD
Method resume_learning()
Resume learning the escalation rule. This function updates the original
EscalationRule object.
Usage
EscalationRule$resume_learning(iter)
Arguments
iterA number of additional iterations.
Returns
An updated EscalationRule object.
Method set_info()
Set information when learning the escalation rule.
Usage
EscalationRule$set_info(info, input, log, checkpoints)
Arguments
infoInformation when learning the escalation rule.
inputInputs for learning the escalation rule.
logThe log of scores during the learning of the escalation rule.
checkpointsThe paths to the directories where each checkpoint is stored.
Method print()
Print function for EscalationRule object
Usage
EscalationRule$print()
Method clone()
The objects of this class are cloneable with this method.
Usage
EscalationRule$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
RLescalation documentation built on April 4, 2025, 2:09 a.m.
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| EscalationRule | R Documentation |
EscalationRule Class
Description
This class represents an escalation rule that generates a next escalation.
Public fields
policyThe RLlib policy that is a Python object.
dirDirectory path of the escalation rule (policy).
dirpathFull path to the directory of the escalation rule.
created_atCreated time of this object.
infoInformation when learning the escalation rule.
inputInputs for learning the escalation rule.
logThe log of scores during the learning of the escalation rule.
checkpointsThe integer vector of iteration counts for checkpoints.
checkpoints_pathsThe paths to the directories where each checkpoint is stored.
Methods
Public methods
Method new()
Create a new EscalationRule object.
Usage
EscalationRule$new(dir = "latest", base_dir = "escalation_rules")
Arguments
dirA character value. A directory name or path where an escalation rule is outputted. By default, the latest escalation rule is searched in 'base_dir'.
base_dirA character value. A directory path that is used as the parent directory if the 'dir' argument is a directory name and is not used otherwise.
Method opt_action()
Compute optimal action probabilities using the obtained escalation rule for data of N and DLT.
Usage
EscalationRule$opt_action(current_dose, data_Ns, data_DLTs)
Arguments
current_doseAn integer value. This is the current dose index, which is within
1:J.data_NsA numeric vector. The cumulative number of patients assigned to each dose in your clinical trial.
data_DLTsA numeric vector. The cumulative number of DLTs corresponding to each dose for the 'data_Ns' argument.
Returns
A character that represents the optimal action. One of the followings: down, stay, up, MTD_1, ..., MTD_J, no_MTD
Method resume_learning()
Resume learning the escalation rule. This function updates the original EscalationRule object.
Usage
EscalationRule$resume_learning(iter)
Arguments
iterA number of additional iterations.
Returns
An updated EscalationRule object.
Method set_info()
Set information when learning the escalation rule.
Usage
EscalationRule$set_info(info, input, log, checkpoints)
Arguments
infoInformation when learning the escalation rule.
inputInputs for learning the escalation rule.
logThe log of scores during the learning of the escalation rule.
checkpointsThe paths to the directories where each checkpoint is stored.
Method print()
Print function for EscalationRule object
Usage
EscalationRule$print()
Method clone()
The objects of this class are cloneable with this method.
Usage
EscalationRule$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
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