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Create CityLearn.py

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  1. CityLearn.py +147 -0
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+ import pickle
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+
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+ import datasets
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+ import numpy as np
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+
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+ _DESCRIPTION = """\
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+ A subset of the D4RL dataset, used for training Decision Transformers
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+ """
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+
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+ _HOMEPAGE = "https://github.com/rail-berkeley/d4rl"
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+
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+ _LICENSE = "Apache-2.0"
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+
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+ # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
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+ # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
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+ _BASE_URL = "https://huggingface.co/datasets/edbeeching/decision_transformer_gym_replay/resolve/main/data"
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+ _URLS = {
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+ "halfcheetah-expert-v2": f"{_BASE_URL}/halfcheetah-expert-v2.pkl",
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+ "halfcheetah-medium-replay-v2": f"{_BASE_URL}/halfcheetah-medium-replay-v2.pkl",
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+ "halfcheetah-medium-v2": f"{_BASE_URL}/halfcheetah-medium-v2.pkl",
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+ "hopper-expert-v2": f"{_BASE_URL}/hopper-expert-v2.pkl",
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+ "hopper-medium-replay-v2": f"{_BASE_URL}/hopper-medium-replay-v2.pkl",
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+ "hopper-medium-v2": f"{_BASE_URL}/hopper-medium-v2.pkl",
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+ "walker2d-expert-v2": f"{_BASE_URL}/walker2d-expert-v2.pkl",
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+ "walker2d-medium-replay-v2": f"{_BASE_URL}/walker2d-medium-replay-v2.pkl",
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+ "walker2d-medium-v2": f"{_BASE_URL}/walker2d-medium-v2.pkl",
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+ }
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+
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+
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+ class DecisionTransformerGymDataset(datasets.GeneratorBasedBuilder):
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+ """The dataset comprises of tuples of (Observations, Actions, Rewards, Dones) sampled
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+ by an expert policy for various continuous control tasks (halfcheetah, hopper, walker2d)"""
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+
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+ VERSION = datasets.Version("1.1.0")
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+
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+ # This is an example of a dataset with multiple configurations.
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+ # If you don't want/need to define several sub-sets in your dataset,
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+ # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
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+
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+ # If you need to make complex sub-parts in the datasets with configurable options
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+ # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
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+ # BUILDER_CONFIG_CLASS = MyBuilderConfig
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+
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+ # You will be able to load one or the other configurations in the following list with
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+ # data = datasets.load_dataset('my_dataset', 'first_domain')
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+ # data = datasets.load_dataset('my_dataset', 'second_domain')
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+ BUILDER_CONFIGS = [
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+ datasets.BuilderConfig(
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+ name="halfcheetah-expert-v2",
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+ version=VERSION,
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+ description="Data sampled from an expert policy in the halfcheetah Mujoco environment",
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+ ),
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+ datasets.BuilderConfig(
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+ name="halfcheetah-medium-replay-v2",
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+ version=VERSION,
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+ description="Data sampled from an medium policy in the halfcheetah Mujoco environment",
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+ ),
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+ datasets.BuilderConfig(
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+ name="halfcheetah-medium-v2",
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+ version=VERSION,
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+ description="Data sampled from an medium policy in the halfcheetah Mujoco environment",
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+ ),
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+ datasets.BuilderConfig(
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+ name="hopper-expert-v2",
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+ version=VERSION,
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+ description="Data sampled from an expert policy in the hopper Mujoco environment",
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+ ),
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+ datasets.BuilderConfig(
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+ name="hopper-medium-replay-v2",
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+ version=VERSION,
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+ description="Data sampled from an medium policy in the hopper Mujoco environment",
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+ ),
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+ datasets.BuilderConfig(
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+ name="hopper-medium-v2",
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+ version=VERSION,
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+ description="Data sampled from an medium policy in the hopper Mujoco environment",
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+ ),
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+ datasets.BuilderConfig(
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+ name="walker2d-expert-v2",
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+ version=VERSION,
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+ description="Data sampled from an expert policy in the halfcheetah Mujoco environment",
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+ ),
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+ datasets.BuilderConfig(
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+ name="walker2d-medium-replay-v2",
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+ version=VERSION,
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+ description="Data sampled from an medium policy in the halfcheetah Mujoco environment",
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+ ),
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+ datasets.BuilderConfig(
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+ name="walker2d-medium-v2",
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+ version=VERSION,
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+ description="Data sampled from an medium policy in the halfcheetah Mujoco environment",
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+ ),
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+ ]
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+
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+ def _info(self):
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+
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+ features = datasets.Features(
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+ {
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+ "observations": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
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+ "actions": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
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+ "rewards": datasets.Sequence(datasets.Value("float32")),
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+ "dones": datasets.Sequence(datasets.Value("bool")),
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+ # These are the features of your dataset like images, labels ...
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+ }
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+ )
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+
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+ return datasets.DatasetInfo(
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+ # This is the description that will appear on the datasets page.
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+ description=_DESCRIPTION,
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+ # This defines the different columns of the dataset and their types
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+ # Here we define them above because they are different between the two configurations
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+ features=features,
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+ # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
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+ # specify them. They'll be used if as_supervised=True in builder.as_dataset.
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+ # supervised_keys=("sentence", "label"),
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+ # Homepage of the dataset for documentation
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+ homepage=_HOMEPAGE,
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+ # License for the dataset if available
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+ license=_LICENSE,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ urls = _URLS[self.config.name]
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+ data_dir = dl_manager.download_and_extract(urls)
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+ return [
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TRAIN,
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+ # These kwargs will be passed to _generate_examples
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+ gen_kwargs={
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+ "filepath": data_dir,
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+ "split": "train",
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+ },
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+ )
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+ ]
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+
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+ # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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+ def _generate_examples(self, filepath, split):
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+ with open(filepath, "rb") as f:
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+ trajectories = pickle.load(f)
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+
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+ for idx, traj in enumerate(trajectories):
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+ yield idx, {
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+ "observations": traj["observations"],
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+ "actions": traj["actions"],
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+ "rewards": np.expand_dims(traj["rewards"], axis=1),
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+ "dones": np.expand_dims(traj.get("dones", traj.get("terminals")), axis=1),
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+ }