from __future__ import annotations
import copy
import importlib.metadata
import os
import re
import warnings
from typing import Any, Callable, Dict, Iterable, List, Optional
import gymnasium as gym
import numpy as np
from gymnasium.core import ActType, ObsType
from gymnasium.envs.registration import EnvSpec
from gymnasium.wrappers import RecordEpisodeStatistics # type: ignore
from minari.data_collector.episode_buffer import EpisodeBuffer
from minari.dataset.minari_dataset import MinariDataset
from minari.dataset.minari_storage import MinariStorage
from minari.serialization import deserialize_space
from minari.storage.datasets_root_dir import get_dataset_path
# Use importlib due to circular import when: "from minari import __version__"
__version__ = importlib.metadata.version("minari")
def validate_datasets_to_combine(
datasets_to_combine: List[MinariDataset],
) -> EnvSpec | None:
"""Check if the given datasets can be combined.
Tests if the datasets were created with the same environment (`env_spec`) and re-calculates the
`max_episode_steps` argument.
Also checks that the datasets obs/act spaces are the same.
Args:
datasets_to_combine (List[MinariDataset]): list of MinariDataset to combine
Returns:
combined_dataset_env_spec (EnvSpec): the resulting EnvSpec of combining the MinariDatasets
"""
# get first among the dataset's env_spec which is not None
first_not_none_env_spec = next(
(
dataset.spec.env_spec
for dataset in datasets_to_combine
if dataset.spec.env_spec is not None
),
None,
)
# early return where all datasets have no env_spec
if first_not_none_env_spec is None:
return None
common_env_spec = copy.deepcopy(first_not_none_env_spec)
# updating the common_env_spec's max_episode_steps & checking equivalence of all env specs
for dataset in datasets_to_combine:
assert isinstance(dataset, MinariDataset)
env_spec = dataset.spec.env_spec
if env_spec is not None:
if (
common_env_spec.max_episode_steps is None
or env_spec.max_episode_steps is None
):
common_env_spec.max_episode_steps = None
else:
common_env_spec.max_episode_steps = max(
common_env_spec.max_episode_steps, env_spec.max_episode_steps
)
# setting max_episode_steps in object's copy to same value for sake of checking equality
env_spec_copy = copy.deepcopy(env_spec)
env_spec_copy.max_episode_steps = common_env_spec.max_episode_steps
if env_spec_copy != common_env_spec:
raise ValueError(
"The datasets to be combined have different values for `env_spec` attribute."
)
else:
raise ValueError(
"Cannot combine datasets having env_spec with those having no env_spec."
)
return common_env_spec
class RandomPolicy:
"""A random action selection policy to compute `ref_min_score`."""
def __init__(self, env: gym.Env):
self.action_space = env.action_space
self.action_space.seed(123)
self.observation_space = env.observation_space
def __call__(self, observation: ObsType) -> ActType:
assert self.observation_space.contains(observation)
return self.action_space.sample()
[docs]
def combine_datasets(datasets_to_combine: List[MinariDataset], new_dataset_id: str):
"""Combine a group of MinariDataset in to a single dataset with its own name id.
The new dataset will contain a metadata attribute `combined_datasets` containing a list
with the dataset names that were combined to form this new Minari dataset.
Args:
datasets_to_combine (list[MinariDataset]): list of datasets to be combined
new_dataset_id (str): name id for the newly created dataset
Returns:
combined_dataset (MinariDataset): the resulting MinariDataset
"""
combined_dataset_env_spec = validate_datasets_to_combine(datasets_to_combine)
new_dataset_path = get_dataset_path(new_dataset_id)
new_dataset_path.mkdir()
new_storage = MinariStorage.new(
new_dataset_path.joinpath("data"),
env_spec=combined_dataset_env_spec,
observation_space=datasets_to_combine[0].observation_space,
action_space=datasets_to_combine[0].action_space,
data_format=datasets_to_combine[0].storage.FORMAT,
)
new_storage.update_metadata(
{
"dataset_id": new_dataset_id,
"combined_datasets": [
dataset.spec.dataset_id for dataset in datasets_to_combine
],
"minari_version": __version__,
}
)
for dataset in datasets_to_combine:
new_storage.update_from_storage(dataset.storage)
return MinariDataset(new_storage)
[docs]
def split_dataset(
dataset: MinariDataset, sizes: List[int], seed: Optional[int] = None
) -> List[MinariDataset]:
"""Split a MinariDataset in multiple datasets.
Args:
dataset (MinariDataset): the MinariDataset to split
sizes (List[int]): sizes of the resulting datasets
seed (Optional[int]): random seed
Returns:
datasets (List[MinariDataset]): resulting list of datasets
"""
if sum(sizes) > dataset.total_episodes:
raise ValueError(
"Incompatible arguments: the sum of sizes exceeds ",
f"the number of episodes in the dataset ({dataset.total_episodes})",
)
generator = np.random.default_rng(seed=seed)
indices = generator.permutation(dataset.episode_indices)
out_datasets = []
start_idx = 0
for length in sizes:
end_idx = start_idx + length
slice_dataset = MinariDataset(
dataset.spec.data_path, indices[start_idx:end_idx]
)
out_datasets.append(slice_dataset)
start_idx = end_idx
return out_datasets
def get_average_reference_score(
env: gym.Env,
policy: Callable[[ObsType], ActType],
num_episodes: int,
) -> float:
env = RecordEpisodeStatistics(env, num_episodes)
episode_returns = []
obs, _ = env.reset(seed=123)
for _ in range(num_episodes):
while True:
action = policy(obs)
obs, _, terminated, truncated, info = env.step(
action # pyright: ignore[reportGeneralTypeIssues]
)
if terminated or truncated:
episode_returns.append(info["episode"]["r"])
obs, _ = env.reset()
break
mean_ref_score = np.mean(episode_returns, dtype=np.float32)
return float(mean_ref_score)
def _generate_dataset_path(dataset_id: str) -> str | os.PathLike:
"""Checks if the dataset already exists locally, then create and return the data storage directory."""
dataset_path = get_dataset_path(dataset_id)
if os.path.exists(dataset_path):
raise ValueError(
f"A Minari dataset with ID {dataset_id} already exists and it cannot be overridden. Please use a different dataset name or version."
)
dataset_path = os.path.join(dataset_path, "data")
os.makedirs(dataset_path)
return dataset_path
def _generate_dataset_metadata(
dataset_id: str,
env_spec: Optional[EnvSpec],
eval_env: Optional[str | gym.Env | EnvSpec],
algorithm_name: Optional[str],
author: Optional[str | set[str]],
author_email: Optional[str | set[str]],
code_permalink: Optional[str],
ref_min_score: Optional[float],
ref_max_score: Optional[float],
expert_policy: Optional[Callable[[ObsType], ActType]],
num_episodes_average_score: int,
description: Optional[str],
requirements: Optional[list],
) -> Dict[str, Any]:
"""Return the metadata dictionary of the dataset."""
dataset_metadata: Dict[str, Any] = {
"dataset_id": dataset_id,
}
# NoneType warnings
if code_permalink is None:
warnings.warn(
"`code_permalink` is set to None. For reproducibility purposes it is highly recommended to link your dataset to versioned code.",
UserWarning,
)
else:
dataset_metadata["code_permalink"] = code_permalink
if author is None:
warnings.warn(
"`author` is set to None. For longevity purposes it is highly recommended to provide an author name.",
UserWarning,
)
else:
dataset_metadata["author"] = {author} if isinstance(author, str) else author
if author_email is None:
warnings.warn(
"`author_email` is set to None. For longevity purposes it is highly recommended to provide an author email, or some other obvious contact information.",
UserWarning,
)
else:
dataset_metadata["author_email"] = (
{author_email} if isinstance(author_email, str) else author_email
)
if algorithm_name is None:
warnings.warn(
"`algorithm_name` is set to None. For reproducibility purpose it's highly recommended to set your algorithm",
UserWarning,
)
else:
dataset_metadata["algorithm_name"] = algorithm_name
if description is None:
warnings.warn(
"`description` is set to None. For longevity purposes it is highly recommended to provide a description of the dataset",
UserWarning,
)
else:
dataset_metadata["description"] = description
dataset_metadata["minari_version"] = __version__
if expert_policy is not None and ref_max_score is not None:
raise ValueError(
"Can't pass a value for `expert_policy` and `ref_max_score` at the same time."
)
if eval_env is None:
warnings.warn(
f"`eval_env` is set to None. If another environment is intended to be used for evaluation please specify corresponding Gymnasium environment (gym.Env | gym.envs.registration.EnvSpec). "
f"If None the environment used to collect the data (`env={env_spec}`) will be used for this purpose.",
UserWarning,
)
eval_env_spec = env_spec
else:
if isinstance(eval_env, str):
eval_env_spec = gym.spec(eval_env)
elif isinstance(eval_env, EnvSpec):
eval_env_spec = eval_env
elif isinstance(eval_env, gym.Env):
eval_env_spec = eval_env.spec
else:
raise ValueError(
"The `eval_env` argument must be of types str|EnvSpec|gym.Env"
)
assert eval_env_spec is not None
dataset_metadata["eval_env_spec"] = eval_env_spec.to_json()
if env_spec is None:
warnings.warn(
"env_spec is None, no environment spec is provided during collection for this dataset",
UserWarning,
)
if eval_env_spec is not None and (
expert_policy is not None or ref_max_score is not None
):
env_ref_score = gym.make(eval_env_spec)
if ref_min_score is None:
ref_min_score = get_average_reference_score(
env_ref_score, RandomPolicy(env_ref_score), num_episodes_average_score
)
if expert_policy is not None:
ref_max_score = get_average_reference_score(
env_ref_score, expert_policy, num_episodes_average_score
)
dataset_metadata["ref_max_score"] = ref_max_score
dataset_metadata["ref_min_score"] = ref_min_score
dataset_metadata["num_episodes_average_score"] = num_episodes_average_score
if requirements is not None:
dataset_metadata["requirements"] = requirements
return dataset_metadata
[docs]
def create_dataset_from_buffers(
dataset_id: str,
buffer: List[EpisodeBuffer],
env: Optional[str | gym.Env | EnvSpec] = None,
eval_env: Optional[str | gym.Env | EnvSpec] = None,
algorithm_name: Optional[str] = None,
author: Optional[str | set[str]] = None,
author_email: Optional[str | set[str]] = None,
code_permalink: Optional[str] = None,
action_space: Optional[gym.spaces.Space] = None,
observation_space: Optional[gym.spaces.Space] = None,
ref_min_score: Optional[float] = None,
ref_max_score: Optional[float] = None,
expert_policy: Optional[Callable[[ObsType], ActType]] = None,
num_episodes_average_score: int = 100,
description: Optional[str] = None,
data_format: Optional[str] = None,
requirements: Optional[list] = None,
):
"""Create Minari dataset from a list of episode dictionary buffers.
The ``dataset_id`` parameter corresponds to the name of the dataset, with the syntax as follows:
``(namespace/)(env_name/)dataset_name(-v[version])`` where ``env_name`` identifies, if present, the name of the environment used to generate the dataset ``dataset_name`` and ``namespace`` optionally groups datasets together.
This ``dataset_id`` is used to load the Minari datasets with :meth:`minari.load_dataset`.
Args:
dataset_id (str): name id to identify Minari dataset.
buffer (list[EpisodeBuffer]): list of episode buffer with data.
env (str | gym.Env | EnvSpec, optional): Gymnasium environment(gym.Env)/environment id(str)/environment spec(EnvSpec) used to collect the buffer data. Defaults to None.
eval_env (str | gym.Env | EnvSpec, optional): Gymnasium environment(gym.Env)/environment id(str)/environment spec(EnvSpec) to use for evaluation with the dataset. After loading the dataset, the environment can be recovered as follows: `MinariDataset.recover_environment(eval_env=True).
If None, and if the `env` used to collect the buffer data is available, latter will be used for evaluation.
algorithm_name (str, optional): name of the algorithm used to collect the data. Defaults to None.
author (str | set, optional): name of the author(s) that generated the dataset. Defaults to None.
author_email (str | set, optional): email(s) of the author(s) that generated the dataset. Defaults to None.
code_permalink (str, optional): link to relevant code used to generate the dataset. Defaults to None.
ref_min_score (float, optional): minimum reference score from the average returns of a random policy. This value is later used to normalize a score with :meth:`minari.get_normalized_score`. If default None the value will be estimated with a default random policy.
Also note that this attribute will be added to the Minari dataset only if `ref_max_score` or `expert_policy` are assigned a valid value other than None.
ref_max_score (float, optional): maximum reference score from the average returns of a hypothetical expert policy. This value is used in `MinariDataset.get_normalized_score()`. Default None.
expert_policy (Callable[[ObsType], ActType], optional): policy to compute `ref_max_score` by averaging the returns over a number of episodes equal to `num_episodes_average_score`.
`ref_max_score` and `expert_policy` can't be passed at the same time. Default to None
num_episodes_average_score (int): number of episodes to average over the returns to compute `ref_min_score` and `ref_max_score`. Default to 100.
observation_space:
action_space (gym.spaces.Space, optional): action space of the environment. If None (default) use the environment action space.
observation_space (gym.spaces.Space, optional): observation space of the environment. If None (default) use the environment observation space.
description (str, optional): description of the dataset being created. Defaults to None.
data_format (str, optional): Data format to store the data in the Minari dataset. If None (defaults), it will use the default format of MinariStorage.
requirements (list of str, optional): list of requirements in pip-style to load the environment and reproduce the dataset. For example, `mujoco>=3.1.0,<3.2.0`, which indicate the supported version range for mujoco package. Defaults to None.
Returns:
MinariDataset
"""
dataset_path = _generate_dataset_path(dataset_id)
if env is None:
env_spec = None
if observation_space is None or action_space is None:
raise ValueError(
"Both observation space and action space must be provided, if env is None"
)
else:
if isinstance(env, EnvSpec):
env_spec = env
elif isinstance(env, str):
env_spec = gym.spec(env)
elif isinstance(env, gym.Env):
env_spec = env.spec
else:
raise TypeError("Unsupported env type")
gym_env: gym.Env = gym.make(env) if isinstance(env, (str, EnvSpec)) else env
observation_space = observation_space or gym_env.observation_space
action_space = action_space or gym_env.action_space
metadata = _generate_dataset_metadata(
dataset_id,
env_spec,
eval_env,
algorithm_name,
author,
author_email,
code_permalink,
ref_min_score,
ref_max_score,
expert_policy,
num_episodes_average_score,
description,
requirements,
)
data_format_kwarg = {"data_format": data_format} if data_format is not None else {}
storage = MinariStorage.new(
dataset_path,
observation_space=observation_space,
action_space=action_space,
env_spec=env_spec,
**data_format_kwarg,
)
storage.update_metadata(metadata)
storage.update_episodes(buffer)
return MinariDataset(storage)
[docs]
def get_normalized_score(dataset: MinariDataset, returns: np.ndarray) -> np.ndarray:
r"""Normalize undiscounted return of an episode.
This function was originally provided in the `D4RL repository <https://github.com/Farama-Foundation/D4RL/blob/71a9549f2091accff93eeff68f1f3ab2c0e0a288/d4rl/offline_env.py#L71>`_.
The computed normalized episode return (normalized score) facilitates the comparison of algorithm performance across different tasks. The returned normalized score will be in a range between 0 and 1.
Where 0 corresponds to the minimum reference score calculated as the average of episode returns collected from a random policy in the environment, and 1 corresponds to a maximum reference score computed as
the average of episode returns from an hypothetical expert policy. These two values are stored as optional attributes in a MinariDataset as `ref_min_score` and `ref_max_score` respectively.
The formula to normalize an episode return is:
.. math:: normalize\_score = \frac{return - ref\_min\_score}{ref\_max\_score - ref\_min\_score}
.. warning:: This utility function is under testing and will not be available in every Minari dataset. For now, only the datasets imported from D4RL will contain the `ref_min_score` and `ref_max_score` attributes.
Args:
dataset (MinariDataset): the MinariDataset with respect to which normalize the score. Must contain the reference score attributes `ref_min_score` and `ref_max_score`.
returns (np.ndarray): a single value or array of episode undiscounted returns to normalize.
Returns:
normalized_scores
"""
ref_min_score = dataset.storage.metadata.get("ref_min_score")
ref_max_score = dataset.storage.metadata.get("ref_max_score")
if ref_min_score is None or ref_max_score is None:
raise ValueError(
f"Reference score not provided for dataset {dataset.spec.dataset_id}. Can't compute the normalized score."
)
return (returns - ref_min_score) / (ref_max_score - ref_min_score)
def get_env_spec_dict(env_spec: EnvSpec) -> Dict[str, str]:
"""Create dict of the environment specs, including observation and action space."""
try:
env = gym.make(env_spec)
action_space_table = env.action_space.__repr__().replace("\n", "")
observation_space_table = env.observation_space.__repr__().replace("\n", "")
except Exception as e:
warnings.warn(f"Failed to make env {env_spec.id}, {e}")
action_space_table, observation_space_table = None, None
md_dict = {"ID": env_spec.id}
if observation_space_table is not None:
md_dict["Observation Space"] = f"`{re.sub(' +', ' ', observation_space_table)}`"
if action_space_table is not None:
md_dict["Action Space"] = f"`{re.sub(' +', ' ', action_space_table)}`"
md_dict.update(
{
"entry_point": f"`{env_spec.entry_point}`",
"max_episode_steps": str(env_spec.max_episode_steps),
"reward_threshold": str(env_spec.reward_threshold),
"nondeterministic": f"`{env_spec.nondeterministic}`",
"order_enforce": f"`{env_spec.order_enforce}`",
"disable_env_checker": f"`{env_spec.disable_env_checker}`",
"kwargs": f"`{env_spec.kwargs}`",
"additional_wrappers": f"`{env_spec.additional_wrappers}`",
"vector_entry_point": f"`{env_spec.vector_entry_point}`",
}
)
return {k: str(v) for k, v in md_dict.items()}
def get_dataset_spec_dict(dataset_spec: Dict) -> Dict[str, str]:
"""Create dict of the dataset specs, including observation and action space."""
code_link = dataset_spec.get("code_permalink")
action_space = dataset_spec.get("action_space")
obs_space = dataset_spec.get("observation_space")
md_dict = {
"Total Steps": str(dataset_spec["total_steps"]),
"Total Episodes": str(dataset_spec["total_episodes"]),
}
if obs_space is not None:
if isinstance(obs_space, (dict, str)):
obs_space = deserialize_space(obs_space)
dataset_observation_space = obs_space.__repr__().replace("\n", "")
md_dict["Dataset Observation Space"] = f"`{dataset_observation_space}`"
if action_space is not None:
if isinstance(action_space, (dict, str)):
action_space = deserialize_space(action_space)
dataset_action_space = action_space.__repr__().replace("\n", "")
md_dict["Dataset Action Space"] = f"`{dataset_action_space}`"
from minari import supported_dataset_versions
version = dataset_spec["minari_version"]
supported = (
"supported" if version in supported_dataset_versions else "not supported"
)
author = dataset_spec.get("author", "Not provided")
if not isinstance(author, str) and isinstance(author, Iterable):
author = ", ".join(author)
email = dataset_spec.get("author_email", "Not provided")
if not isinstance(email, str) and isinstance(email, Iterable):
email = ", ".join(email)
assert isinstance(author, str)
assert isinstance(email, str)
md_dict.update(
{
"Algorithm": dataset_spec.get("algorithm_name", "Not provided"),
"Author": author,
"Email": email,
"Code Permalink": f"[{code_link}]({code_link})",
"Minari Version": f"`{version}` ({supported})",
"Download": f"`minari download {dataset_spec['dataset_id']}`",
}
)
return md_dict
