Update cartpole_gym_replay.py

cartpole_gym_replay.py

@@ -0,0 +1,65 @@
+import pickle
+
+import datasets
+import numpy as np
+
+# 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/marktrovinger/cartpole_gym_replay/resolve/main"
+_DATA_URL = f"{_BASE_URL}/replay_buffer_npz.npz"
+
+
+
+class DecisionTransformerGymDataset(datasets.GeneratorBasedBuilder):
+    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("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:
+            #trajectories = pickle.load(f)
+            trajectories = np.load(f)
+
+            for idx, traj in enumerate(trajectories):
+                yield idx, {
+                    "observations": traj["observations"],
+                    "actions": traj["actions"],
+                    "rewards": traj["rewards"],
+                    "dones": traj["dones"],
+                }