trying stochastic stuff

src/agentlab/experiments/custom_benchmark.py

@@ -4,9 +4,11 @@
 from typing import Optional
 
 import benchmarks
+import numpy as np
 import pandas as pd
 from bgym import Benchmark, EnvArgs, HighLevelActionSetArgs
 from browsergym.experiments.benchmark.base import BenchmarkBackend
+from browsergym.experiments.benchmark.utils import make_env_args_list_from_repeat_tasks
 from dataclasses_json import DataClassJsonMixin, config
 from torch import threshold
 
@@ -66,10 +68,10 @@ def select(self, values, env_args_list):
 class AllTasksBenchmark(ResampleBenchmark):
     def evaluate(self, study, env_args_list):
         return [0] * len(env_args_list)
-    
+
     def select(self, values, env_args_list):
         return env_args_list
-    
+
 
 @dataclass
 class HighVarianceBenchmark(ResampleBenchmark):
@@ -87,8 +89,47 @@ def select(self, values, env_args_list):
         return selected_env_args
 
 
+@dataclass
+class StochasticHighVarianceBenchmark(ResampleBenchmark):
+    regulation_threshold: float = 0.1
+    total_seeds = 600
+    min_seeds = 2
+    random_seed = 42
+
+    def evaluate(self, study: Study, env_args_list):
+        result_df = load_result_df(study.dir)
+        var = result_df.groupby("env.task_name")["cum_reward"].var()
+        probs = dict((var + self.regulation_threshold) / (var + self.regulation_threshold).sum())
+        return probs
+
+    def select(self, values, env_args_list: list[EnvArgs]):
+        selected_env_args = []
+        max_steps = env_args_list[0].max_steps
+        for task_name, p in values.items():
+            # ceil to avoid missing seeds
+            n_seeds = np.random.RandomState(self.random_seed).poisson(p * self.total_seeds)
+            n_seeds = max(n_seeds, self.min_seeds)
+            for seed in np.random.RandomState(self.random_seed).randint(0, 2**32, n_seeds):
+                selected_env_args.append(
+                    EnvArgs(
+                        task_name=task_name,
+                        task_seed=int(seed),
+                        max_steps=max_steps,
+                        headless=True,
+                        record_video=False,
+                        wait_for_user_message=False,
+                        viewport=None,
+                        slow_mo=None,
+                        storage_state=None,
+                        task_kwargs=None,
+                    )
+                )
+        return selected_env_args
+
+
 if __name__ == "__main__":
-    exp_dir = Path("/home/t/agentlab_results/2025-02-26_10-15-04_genericagent-gpt-4o-mini-2024-07-18-on-miniwob-tiny-test")
-    benchmark = HighVarianceBenchmark(exp_dir=exp_dir)
+    exp_dir = Path(
+        "/Users/t.lesellierdechezell/agentlab_results/2025-03-04_14-43-48_genericagent-gpt-4o-mini-2024-07-18-on-miniwob"
+    )
+    benchmark = StochasticHighVarianceBenchmark(exp_dir=exp_dir)
     print(benchmark.env_args_list)
-