Create CityLearn.py

CityLearn.py

@@ -0,0 +1,147 @@
+import pickle
+
+import datasets
+import numpy as np
+
+_DESCRIPTION = """\
+A subset of the D4RL dataset, used for training Decision Transformers
+"""
+
+_HOMEPAGE = "https://github.com/rail-berkeley/d4rl"
+
+_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/edbeeching/decision_transformer_gym_replay/resolve/main/data"
+_URLS = {
+    "halfcheetah-expert-v2": f"{_BASE_URL}/halfcheetah-expert-v2.pkl",
+    "halfcheetah-medium-replay-v2": f"{_BASE_URL}/halfcheetah-medium-replay-v2.pkl",
+    "halfcheetah-medium-v2": f"{_BASE_URL}/halfcheetah-medium-v2.pkl",
+    "hopper-expert-v2": f"{_BASE_URL}/hopper-expert-v2.pkl",
+    "hopper-medium-replay-v2": f"{_BASE_URL}/hopper-medium-replay-v2.pkl",
+    "hopper-medium-v2": f"{_BASE_URL}/hopper-medium-v2.pkl",
+    "walker2d-expert-v2": f"{_BASE_URL}/walker2d-expert-v2.pkl",
+    "walker2d-medium-replay-v2": f"{_BASE_URL}/walker2d-medium-replay-v2.pkl",
+    "walker2d-medium-v2": f"{_BASE_URL}/walker2d-medium-v2.pkl",
+}
+
+
+class DecisionTransformerGymDataset(datasets.GeneratorBasedBuilder):
+    """The dataset comprises of tuples of (Observations, Actions, Rewards, Dones) sampled
+    by an expert policy for various continuous control tasks (halfcheetah, hopper, walker2d)"""
+
+    VERSION = datasets.Version("1.1.0")
+
+    # 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="halfcheetah-expert-v2",
+            version=VERSION,
+            description="Data sampled from an expert policy in the halfcheetah Mujoco environment",
+        ),
+        datasets.BuilderConfig(
+            name="halfcheetah-medium-replay-v2",
+            version=VERSION,
+            description="Data sampled from an medium policy in the halfcheetah Mujoco environment",
+        ),
+        datasets.BuilderConfig(
+            name="halfcheetah-medium-v2",
+            version=VERSION,
+            description="Data sampled from an medium policy in the halfcheetah Mujoco environment",
+        ),
+        datasets.BuilderConfig(
+            name="hopper-expert-v2",
+            version=VERSION,
+            description="Data sampled from an expert policy in the hopper Mujoco environment",
+        ),
+        datasets.BuilderConfig(
+            name="hopper-medium-replay-v2",
+            version=VERSION,
+            description="Data sampled from an medium policy in the hopper Mujoco environment",
+        ),
+        datasets.BuilderConfig(
+            name="hopper-medium-v2",
+            version=VERSION,
+            description="Data sampled from an medium policy in the hopper Mujoco environment",
+        ),
+        datasets.BuilderConfig(
+            name="walker2d-expert-v2",
+            version=VERSION,
+            description="Data sampled from an expert policy in the halfcheetah Mujoco environment",
+        ),
+        datasets.BuilderConfig(
+            name="walker2d-medium-replay-v2",
+            version=VERSION,
+            description="Data sampled from an medium policy in the halfcheetah Mujoco environment",
+        ),
+        datasets.BuilderConfig(
+            name="walker2d-medium-v2",
+            version=VERSION,
+            description="Data sampled from an medium policy in the halfcheetah Mujoco environment",
+        ),
+    ]
+
+    def _info(self):
+
+        features = datasets.Features(
+            {
+                "observations": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
+                "actions": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
+                "rewards": datasets.Sequence(datasets.Value("float32")),
+                "dones": datasets.Sequence(datasets.Value("bool")),
+                # 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:
+            trajectories = pickle.load(f)
+
+            for idx, traj in enumerate(trajectories):
+                yield idx, {
+                    "observations": traj["observations"],
+                    "actions": traj["actions"],
+                    "rewards": np.expand_dims(traj["rewards"], axis=1),
+                    "dones": np.expand_dims(traj.get("dones", traj.get("terminals")), axis=1),
+                }