Update parquet files

README.md

@@ -1,3 +0,0 @@
----
-license: mit
----

cartpole_gym_replay.py

@@ -1,75 +0,0 @@
-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"
-
-_DESCRIPTION = """ \
-Testing a cartpole replay.
-"""
-
-_HOMEPAGE = "blah"
-
-_LICENSE = "MIT"
-
-
-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 = _DATA_URL
-        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):
-        
-        #trajectories = pickle.load(f)
-        #traj = np.load(filepath)
-        obs = np.load(f'{filepath}/observations.npy')
-        act = np.load(f'{filepath}/actions.npy')
-        rew = np.load(f'{filepath}/rewards.npy')
-        dones = np.load(f'{filepath}/dones.npy')
-        for idx, value in enumerate(obs):
-            yield idx, {
-                "observations": obs[idx],
-                "actions": act[idx],
-                "rewards": rew[idx],
-                "dones": dones[idx],
-            }

replay_buffer_npz.npz → default/cartpole_gym_replay-train.parquet

@@ -1,3 +1,3 @@
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-size 15197224
+oid sha256:cd0415637e6f3211ce1c11205aee932f37dd8a47a2e7318dd82f6a4fb86a0136
+size 23449043

replay_buffer_pkl.pkl

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-oid sha256:bee243999872c07ea2cac3132d38814cc81c9e08c40a3e97bc6450e5f0f1625f
-size 52003748