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Pong-v4-expert-MCTS / Pong-v4-expert-MCTS.py
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Update Pong-v4-expert-MCTS.py
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import pickle
from safetensors import saveopen
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.safetensors",
}
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):
data = {}
with safe_open(filepath, framework="pt", device="cpu") as f:
for key in f.keys():
data[key] = f.get_tensor(key)
for idx in range(len(data['obs'])):
yield idx, {
'observation': data['obs'][idx],
'action': data['actions'][idx],
'hidden_state': data['hidden_state'][idx],
}