We have restored the parameter values to their previous values.

R/rl_config_set.R

@@ -9,7 +9,7 @@
 #' @param cores A positive integer value. Number of CPU cores used for learning.
 #' @param gamma A positive numeric value. Discount factor of the Markov decision
 #'        process. Default is 1.0 (not discount).
-#' @param lr A positive numeric value. Learning rate (default 3e-5). You can set
+#' @param lr A positive numeric value. Learning rate (default 5e-5). You can set
 #'        a learning schedule instead of a learning rate.
 #' @param train_batch_size A positive integer value. Training batch size.
 #'        Deprecated on the new API stack.
@@ -41,7 +41,7 @@ rl_config_set <- function(iter = 1000L,
                           save_every_iter = NULL,
                           cores = 4L,
                           # Common settings
-                          gamma = 1.0, lr = 3e-5,
+                          gamma = 1.0, lr = 5e-5,
                           train_batch_size = 10000L, model = rl_dnn_config(),
                           # PPO specific settings
                           sgd_minibatch_size = 200L, num_sgd_iter = 20L,

R/utils.R

@@ -9,7 +9,7 @@ compute_state <- function(current_dose, J, data_Ns, data_DLTs, N_total) {
   stopifnot(length(data_Ns) == length(data_DLTs))
   stopifnot(all(data_Ns >= data_DLTs))
 
-  is_final <- ifelse(sum(data_Ns) == N_total, 1.0, 0.0)
+  is_final <- ifelse(sum(data_Ns) == N_total, 0.2, 0.1)
 
   state <- as.array(c(
     (current_dose - 1) / J,

README.Rmd

@@ -67,8 +67,8 @@ escalation_rule <- learn_escalation_rule(
 
 escalation_rule
 #> <EscalationRule>
-#> dir: escalation_rules/20241231_130626
-#> created at: 2024-12-31 14:23:31
+#> dir: escalation_rules/20250101_162633
+#> created at: 2025-01-01 17:43:23
 #> call:
 #> 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))
@@ -141,12 +141,14 @@ library(dplyr)
 
 MTD_true <-  list("MTD_6", c("MTD_3", "MTD_4"), "no_MTD", "MTD_4")
 
-d_sim |> 
+d_res <- d_sim |> 
   filter(cohortID == max(cohortID), .by = c(scenarioID, simID)) |> 
   rowwise() |>
   mutate(correct = if_else(recommended %in% MTD_true[[scenarioID]], 1, 0)) |>
   ungroup() |> 
   summarise(PCS = mean(correct), .by = scenarioID)
+
+d_res
 #> # A tibble: 4 × 2
 #>   scenarioID   PCS
 #>        <int> <dbl>

README.md

@@ -61,8 +61,8 @@ escalation_rule <- learn_escalation_rule(
 
 escalation_rule
 #> <EscalationRule>
-#> dir: escalation_rules/20241231_130626
-#> created at: 2024-12-31 14:23:31
+#> dir: escalation_rules/20250101_162633
+#> created at: 2025-01-01 17:43:23
 #> call:
 #> 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))
@@ -143,12 +143,14 @@ library(dplyr)
 
 MTD_true <-  list("MTD_6", c("MTD_3", "MTD_4"), "no_MTD", "MTD_4")
 
-d_sim |> 
+d_res <- d_sim |> 
   filter(cohortID == max(cohortID), .by = c(scenarioID, simID)) |> 
   rowwise() |>
   mutate(correct = if_else(recommended %in% MTD_true[[scenarioID]], 1, 0)) |>
   ungroup() |> 
   summarise(PCS = mean(correct), .by = scenarioID)
+
+d_res
 #> # A tibble: 4 × 2
 #>   scenarioID   PCS
 #>        <int> <dbl>

inst/python/DoseEscalationEnv.py

@@ -83,7 +83,7 @@ def _compute_state(self):
         Returns:
             The specific value of the state s.
         """
-        is_final = 1.0 if np.sum(self.Ns) == self.N_total else 0.0
+        is_final = 0.2 if np.sum(self.Ns) == self.N_total else 0.1
 
         return np.concatenate((
             np.array([self.current_dose / (self.J - 1)]),

vignettes/RLescalation.Rmd

@@ -71,8 +71,8 @@ escalation_rule <- learn_escalation_rule(
 
 escalation_rule
 #> <EscalationRule>
-#> dir: escalation_rules/20241231_130626
-#> created at: 2024-12-31 14:23:31
+#> dir: escalation_rules/20250101_162633
+#> created at: 2025-01-01 17:43:23
 #> call:
 #> 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))
@@ -145,12 +145,14 @@ library(dplyr)
 
 MTD_true <-  list("MTD_6", c("MTD_3", "MTD_4"), "no_MTD", "MTD_4")
 
-d_sim |> 
+d_res <- d_sim |> 
   filter(cohortID == max(cohortID), .by = c(scenarioID, simID)) |> 
   rowwise() |>
   mutate(correct = if_else(recommended %in% MTD_true[[scenarioID]], 1, 0)) |>
   ungroup() |> 
   summarise(PCS = mean(correct), .by = scenarioID)
+
+d_res
 #> # A tibble: 4 × 2
 #>   scenarioID   PCS
 #>        <int> <dbl>