Benchmarks

browsergym/experiments/requirements.txt

@@ -1,2 +1,3 @@
 browsergym-core==0.8.1
 tiktoken>=0.4
+dataclasses-json

browsergym/experiments/src/bgym/__init__.py

@@ -1,12 +1,13 @@
+from browsergym.core.action.base import AbstractActionSet
+from browsergym.core.action.highlevel import HighLevelActionSet
+from browsergym.core.action.python import PythonActionSet
 from browsergym.experiments.agent import Agent, AgentInfo
+from browsergym.experiments.benchmark import Benchmark, HighLevelActionSetArgs
 from browsergym.experiments.loop import (
+    AbstractAgentArgs,
     EnvArgs,
     ExpArgs,
-    AbstractAgentArgs,
     ExpResult,
     StepInfo,
     StepTimestamps,
 )
-from browsergym.core.action.base import AbstractActionSet
-from browsergym.core.action.highlevel import HighLevelActionSet
-from browsergym.core.action.python import PythonActionSet

browsergym/experiments/src/browsergym/experiments/benchmark.py

@@ -0,0 +1,376 @@
+import fnmatch
+import io
+import logging
+import pkgutil
+from dataclasses import dataclass, field
+from typing import Literal, Optional
+
+import numpy as np
+import pandas as pd
+from dataclasses_json import DataClassJsonMixin, config
+
+from browsergym.core.action.highlevel import HighLevelActionSet
+from browsergym.experiments.loop import SEED_MAX, EnvArgs
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class HighLevelActionSetArgs(DataClassJsonMixin):
+    subsets: list[HighLevelActionSet.ActionSubset]
+    # custom_actions: list[callable] | None  # non-serializable argument, not supported
+    multiaction: bool
+    strict: bool
+    retry_with_force: bool
+    demo_mode: Literal["off", "default", "all_blue", "only_visible_elements"]
+
+    def make_action_set(self):
+        return HighLevelActionSet(
+            subsets=self.subsets,
+            custom_actions=None,
+            multiaction=self.multiaction,
+            strict=self.strict,
+            retry_with_force=self.retry_with_force,
+            demo_mode=self.demo_mode,
+        )
+
+
+@dataclass
+class Benchmark(DataClassJsonMixin):
+    name: str
+    high_level_action_set_args: HighLevelActionSetArgs
+    env_args_list: list[EnvArgs]
+    task_metadata: Optional[pd.DataFrame] = field(
+        default_factory=lambda: None,
+        metadata=config(
+            encoder=lambda df: df.to_dict(orient="records") if df is not None else None,
+            decoder=lambda items: pd.DataFrame(items) if items is not None else None,
+        ),
+    )
+
+    def __post_init__(self):
+        # if no metadata is present, generate a dataframe with single "task_name" column
+        if self.task_metadata is None:
+            unique_task_names = list(set([env_args.task_name for env_args in self.env_args_list]))
+            self.task_metadata = pd.DataFrame(
+                [{"task_name": task_name} for task_name in unique_task_names]
+            )
+        # make sure all tasks in env_args are in the metadata
+        metadata_tasks = list(self.task_metadata["task_name"])
+        assert all([env_args.task_name in metadata_tasks for env_args in self.env_args_list])
+
+    def subset_from_split(self, split: Literal["train", "eval", "test"]):
+        split_column = "split"
+
+        # check for a split column in metadata
+        if not split_column in self.task_metadata.columns:
+            raise NotImplementedError(
+                f"This benchmark does not provide train/eval/test {split_column} (missing split column in task metadata)"
+            )
+
+        # recover the target split
+        sub_benchmark = self.subset_from_regexp(split_column, regexp=f"^{split}$")
+
+        # check that the split exists (non-empty task list)
+        if not sub_benchmark.env_args_list:
+            raise ValueError(f"The {split} split for this benchmark is empty.")
+
+        return sub_benchmark
+
+    def subset_from_glob(self, column, glob):
+        subset = self.subset_from_regexp(column, regexp=fnmatch.translate(glob))
+        subset.name = f"{self.name}[{column}={glob}]"
+        return subset
+
+    def subset_from_regexp(self, column, regexp):
+        # extract the filtered task_name subset
+        task_name_subset = task_list_from_metadata(self.task_metadata, {column: regexp})
+
+        # return the sub benchmark
+        return Benchmark(
+            name=f"{self.name}[{column}=/{regexp}/]",
+            high_level_action_set_args=self.high_level_action_set_args,
+            env_args_list=[
+                env_args
+                for env_args in self.env_args_list
+                if env_args.task_name in task_name_subset
+            ],
+            task_metadata=self.task_metadata,
+        )
+
+
+def task_metadata(benchmark_name: str):
+    return task_metadata_from_csv(
+        io.StringIO(
+            pkgutil.get_data(__name__, f"task_metadata/{benchmark_name}.csv").decode("utf-8")
+        )
+    )
+
+
+def task_metadata_from_csv(filepath):
+    return pd.read_csv(filepath).fillna("")
+
+
+def task_list_from_metadata(metadata: pd.DataFrame, filter: dict[str, str] = {}):
+    df = metadata
+    # filter the desired columns (AND filter)
+    for col_name, regex in filter.items():
+        col_filter = df[col_name].astype(str).str.contains(regex, regex=True)
+        df = df[col_filter]
+    # return only the task names
+    return list(df["task_name"])
+
+
+# These are mean as the default highlevel action set to fairly evaluate agents on each benchmark.
+# They are mostly arbitrary, the important thing is to evaluate different agents using the same action set for fairness.
+DEFAULT_HIGHLEVEL_ACTION_SET_ARGS = {
+    # loosely from https://github.com/Farama-Foundation/miniwob-plusplus/blob/1bab0dffe34e92cc1049fe9443542029bf7e44a9/miniwob/action.py#L122
+    "miniwob": HighLevelActionSetArgs(
+        subsets=["bid", "coord"],
+        multiaction=False,
+        strict=False,
+        retry_with_force=False,
+        demo_mode="off",
+    ),
+    # loosely from https://github.com/Farama-Foundation/miniwob-plusplus/blob/1bab0dffe34e92cc1049fe9443542029bf7e44a9/miniwob/action.py#L160
+    "miniwob_bid": HighLevelActionSetArgs(
+        subsets=["bid"],
+        multiaction=False,
+        strict=False,
+        retry_with_force=False,
+        demo_mode="off",
+    ),
+    # loosely from https://github.com/Farama-Foundation/miniwob-plusplus/blob/1bab0dffe34e92cc1049fe9443542029bf7e44a9/miniwob/action.py#L173
+    "miniwob_coord": HighLevelActionSetArgs(
+        subsets=["coord"],
+        multiaction=False,
+        strict=False,
+        retry_with_force=False,
+        demo_mode="off",
+    ),
+    "workarena_l1": HighLevelActionSetArgs(
+        subsets=["chat", "bid"],  # no need for infeasible action
+        multiaction=False,
+        strict=False,
+        retry_with_force=False,
+        demo_mode="off",
+    ),
+    "workarena": HighLevelActionSetArgs(
+        subsets=["chat", "infeas", "bid"],
+        multiaction=False,
+        strict=False,
+        retry_with_force=False,
+        demo_mode="off",
+    ),
+    # from https://arxiv.org/abs/2307.13854
+    "webarena": HighLevelActionSetArgs(
+        subsets=["chat", "infeas", "bid", "tab", "nav"],
+        multiaction=False,
+        strict=False,
+        retry_with_force=False,
+        demo_mode="off",
+    ),
+    # from https://arxiv.org/abs/2401.13649
+    "visualwebarena": HighLevelActionSetArgs(
+        subsets=["chat", "infeas", "bid", "tab", "nav"],
+        multiaction=False,
+        strict=False,
+        retry_with_force=False,
+        demo_mode="off",
+    ),
+}
+
+# all benchmarks are callables designed for lazy loading, i.e. `bench = BENCHMARKS["miniwob_all"]()`
+BENCHMARKS = {
+    "miniwob_all": lambda: Benchmark(
+        name="miniwob_all",
+        high_level_action_set_args=DEFAULT_HIGHLEVEL_ACTION_SET_ARGS["miniwob"],
+        env_args_list=_make_env_args_list_from_repeat_tasks(
+            task_list=task_list_from_metadata(metadata=task_metadata("miniwob")),
+            max_steps=10,
+            n_repeats=10,
+            seeds_rng=np.random.RandomState(42),
+        ),
+        task_metadata=task_metadata("miniwob"),
+    ),
+    "miniwob_webgum": lambda: Benchmark(
+        name="miniwob_webgum",
+        high_level_action_set_args=DEFAULT_HIGHLEVEL_ACTION_SET_ARGS["miniwob"],
+        env_args_list=_make_env_args_list_from_repeat_tasks(
+            task_list=task_list_from_metadata(
+                metadata=task_metadata("miniwob"), filter={"webgum_subset": "True"}
+            ),
+            max_steps=10,
+            n_repeats=10,
+            seeds_rng=np.random.RandomState(42),
+        ),
+        task_metadata=task_metadata("miniwob"),
+    ),
+    "miniwob_tiny_test": lambda: Benchmark(
+        name="miniwob_tiny_test",
+        high_level_action_set_args=DEFAULT_HIGHLEVEL_ACTION_SET_ARGS["miniwob"],
+        env_args_list=_make_env_args_list_from_repeat_tasks(
+            task_list=["miniwob.click-dialog", "miniwob.click-checkboxes"],
+            max_steps=5,
+            n_repeats=2,
+            seeds_rng=np.random.RandomState(42),
+        ),
+        task_metadata=task_metadata("miniwob"),
+    ),
+    "miniwob_train": lambda: Benchmark(
+        name="miniwob_train",
+        high_level_action_set_args=DEFAULT_HIGHLEVEL_ACTION_SET_ARGS["miniwob"],
+        env_args_list=_make_env_args_list_from_repeat_tasks(
+            task_list=task_list_from_metadata(
+                metadata=task_metadata("miniwob"),
+                filter={"miniwob_category": "original|nodelay|debug|additional"},
+            ),
+            max_steps=10,
+            n_repeats=10,
+            seeds_rng=np.random.RandomState(42),
+        ),
+        task_metadata=task_metadata("miniwob"),
+    ),
+    "miniwob_test": lambda: Benchmark(
+        name="miniwob_test",
+        high_level_action_set_args=DEFAULT_HIGHLEVEL_ACTION_SET_ARGS["miniwob"],
+        env_args_list=_make_env_args_list_from_repeat_tasks(
+            task_list=task_list_from_metadata(
+                metadata=task_metadata("miniwob"), filter={"miniwob_category": "hidden test"}
+            ),
+            max_steps=10,
+            n_repeats=10,
+            seeds_rng=np.random.RandomState(42),
+        ),
+        task_metadata=task_metadata("miniwob"),
+    ),
+    "webarena": lambda: Benchmark(
+        name="webarena",
+        high_level_action_set_args=DEFAULT_HIGHLEVEL_ACTION_SET_ARGS["webarena"],
+        env_args_list=_make_env_args_list_from_repeat_tasks(
+            task_list=task_list_from_metadata(metadata=task_metadata("webarena")),
+            max_steps=15,
+            n_repeats=1,
+            seeds_rng=np.random.RandomState(42),
+        ),
+        task_metadata=task_metadata("webarena"),
+    ),
+    "visualwebarena": lambda: Benchmark(
+        name="visualwebarena",
+        high_level_action_set_args=DEFAULT_HIGHLEVEL_ACTION_SET_ARGS["visualwebarena"],
+        env_args_list=_make_env_args_list_from_repeat_tasks(
+            task_list=task_list_from_metadata(metadata=task_metadata("visualwebarena")),
+            max_steps=15,
+            n_repeats=1,
+            seeds_rng=np.random.RandomState(42),
+        ),
+        task_metadata=task_metadata("visualwebarena"),
+    ),
+    "workarena_l1": lambda: Benchmark(
+        name="workarena_l1",
+        high_level_action_set_args=DEFAULT_HIGHLEVEL_ACTION_SET_ARGS["workarena_l1"],
+        env_args_list=_make_env_args_list_from_repeat_tasks(
+            task_list=task_list_from_metadata(
+                metadata=task_metadata("workarena"), filter={"level": "l1"}
+            ),
+            max_steps=15,
+            n_repeats=10,
+            seeds_rng=np.random.RandomState(42),
+        ),
+        task_metadata=task_metadata("workarena"),
+    ),
+    "workarena_l1_sort": lambda: Benchmark(
+        name="workarena_l1_sort",
+        high_level_action_set_args=DEFAULT_HIGHLEVEL_ACTION_SET_ARGS["workarena_l1"],
+        env_args_list=_make_env_args_list_from_repeat_tasks(
+            task_list=task_list_from_metadata(
+                metadata=task_metadata("workarena"), filter={"level": "l1", "category": "list-sort"}
+            ),
+            max_steps=15,
+            n_repeats=10,
+            seeds_rng=np.random.RandomState(42),
+        ),
+        task_metadata=task_metadata("workarena"),
+    ),
+    "workarena_l2_agent_curriculum_eval": lambda: Benchmark(
+        name="workarena_l2_agent_curriculum_eval",
+        high_level_action_set_args=DEFAULT_HIGHLEVEL_ACTION_SET_ARGS["workarena"],
+        env_args_list=_make_env_args_list_from_workarena_curriculum(
+            level="l2",
+            task_category_filter=None,
+            meta_seed=42,  # meta seed for evaluation curriculum
+            max_steps=50,
+            curriculum_type="agent",
+        ),
+        task_metadata=task_metadata("workarena"),
+    ),
+    "workarena_l3_agent_curriculum_eval": lambda: Benchmark(
+        name="workarena_l3_agent_curriculum_eval",
+        high_level_action_set_args=DEFAULT_HIGHLEVEL_ACTION_SET_ARGS["workarena"],
+        env_args_list=_make_env_args_list_from_workarena_curriculum(
+            level="l3",
+            task_category_filter=None,
+            meta_seed=42,  # meta seed for evaluation curriculum
+            max_steps=50,
+            curriculum_type="agent",
+        ),
+        task_metadata=task_metadata("workarena"),
+    ),
+}
+
+
+def _make_env_args_list_from_workarena_curriculum(
+    level: Literal["l2", "l3"],
+    task_category_filter: str,
+    meta_seed: int,
+    max_steps: int,
+    curriculum_type: Literal["human", "agent"],
+):
+    """
+    Returns a WorkArena predefined task curriculum (e.g., task and seed combination).
+    """
+    assert level in ("l2", "l3")
+    assert curriculum_type in ("human", "agent")
+
+    env_args_list = []
+
+    from browsergym.workarena import get_all_tasks_agents
+
+    all_task_tuples = get_all_tasks_agents(
+        filter=f"{level}.{task_category_filter}" if task_category_filter else level,
+        meta_seed=meta_seed,
+        is_agent_curriculum=(curriculum_type == "agent"),
+    )
+
+    for task, seed in all_task_tuples:
+        task_name = task.get_task_id()
+        env_args_list.append(EnvArgs(task_name=task_name, task_seed=seed, max_steps=max_steps))
+
+    return env_args_list
+
+
+def _make_env_args_list_from_repeat_tasks(
+    task_list: list[str], max_steps: int, n_repeats: int, seeds_rng: np.random.RandomState
+):
+    """
+    Generates a list of `len(task_list)` time `n_repeats` environments arguments, using randomly generated seeds.
+    """
+    env_args_list = []
+    for task in task_list:
+        for seed in seeds_rng.randint(low=0, high=SEED_MAX, size=n_repeats):
+            env_args_list.append(
+                EnvArgs(
+                    task_name=task,
+                    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 env_args_list