Pong-v4-expert-MCTS.py

import pickle

import datasets

_DESCRIPTION = """\
Data sampled from an efficient-zero policy in the pong environment. The MCTS hidden state is included in the dataset.
"""

_HOMEPAGE = "https://github.com/opendilab/DI-engine"

_LICENSE = "Apache-2.0"

# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_BASE_URL = "https://huggingface.co/datasets/OpenDILabCommunity/Pong-v4-expert-MCTS/resolve/main"
_URLS = {
    "Pong-v4-expert-MCTS": f"{_BASE_URL}/pong-v4-expert.pkl",
}


class DecisionTransformerGymDataset(datasets.GeneratorBasedBuilder):

    VERSION = datasets.Version("0.0.1")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="Pong-v4-expert-MCTS",
            version=VERSION,
            description="Data sampled from an efficient-zero policy in the pong environment",
        )
    ]

    def _info(self):

        features = datasets.Features(
            {
                "observation": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("uint8")))),
                "action": datasets.Value("int64"),
                "hidden_state": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("float32")))),
                # These are the features of your dataset like images, labels ...
            }
        )

        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            # Here we define them above because they are different between the two configurations
            features=features,
            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
        )

    def _split_generators(self, dl_manager):
        urls = _URLS[self.config.name]
        data_dir = dl_manager.download_and_extract(urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir,
                    "split": "train",
                },
            )
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):
        with open(filepath, "rb") as f:
            data = pickle.load(f)
        
        for idx in range(len(data['obs'])):
            yield idx, {
                'observation': data['obs'][idx],
                'action': data['actions'][idx],
                'hidden_state': data['hidden_state'][idx],
            }