learn_escalation_rule {RLescalation} | R Documentation |
Build an Optimal Dose Escalation Rule using Reinforcement Learning
Description
Build an Optimal Dose Escalation Rule using Reinforcement Learning
Usage
learn_escalation_rule(
J,
target,
epsilon,
delta,
N_total,
N_cohort,
seed = NULL,
rl_config = rl_config_set(),
rl_scenarios = NULL,
output_dir = format(Sys.time(), "%Y%m%d_%H%M%S"),
output_base_dir = "escalation_rules",
checkpoint_dir = "checkpoints"
)
Arguments
J |
A positive integer value. The number of doses. |
target |
A positive numeric value. The target DLT probability. |
epsilon |
A positive numeric value. The acceptable range of target DLT
probabilities is defined as [ |
delta |
A positive numeric value. The unacceptable ranges of target DLT
probabilities are defined as [0, |
N_total |
A positive integer value. The total number of patients. |
N_cohort |
A positive integer value. The number of patients for each cohort. |
seed |
An integer value. Random seed for reinforcement learning. |
rl_config |
A list. Other settings for reinforcement learning. See rl_config_set for details. |
rl_scenarios |
A list. Scenarios used for reinforcement learning.
Default is |
output_dir |
A character value. Directory name or path to store the built escalation rule. Default is the current datetime. |
output_base_dir |
A character value. Parent directory path where the built escalation rule will be stored. Valid only if 'output_dir' does not contain '/'. Default is "escalation_rules". |
checkpoint_dir |
A character value. Parent directory path to save checkpoints. It enables you to resume learning from that point onwards. Default is "checkpoints". |
Value
An EscalationRule object.
Examples
library(RLescalation)
# We obtain an optimal dose escalation rule by executing `learn_escalation_rule()`.
## Not run:
escalation_rule <- learn_escalation_rule(
J = 6, target = 0.25, epsilon = 0.04, delta = 0.1,
N_total = 36, N_cohort = 3, seed = 123,
rl_config = rl_config_set(iter = 1000)
)
## End(Not run)