Upload Pong-v4-expert-MCTS.py

Pong-v4-expert-MCTS.py

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+import pickle
+
+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-MCTS.pkl",
+}
+
+
+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.Sequence(datasets.Value("float32")),
+                "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):
+        with open(filepath, "rb") as f:
+            data = pickle.load(f)
+        
+        for idx in range(len(data['obs'])):
+            yield idx, {
+                'observation': data['obs'][idx],
+                'action': data['actions'][idx],
+                'hidden_state': data['hidden_state'][idx],
+            }