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Datasets:
cameronangliss
/
vgc-battle-logs

Tasks:
Reinforcement Learning
Size:
100K<n<1M
ArXiv:
License:
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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ValueError
Message:      Failed to convert pandas DataFrame to Arrow Table from file hf://datasets/cameronangliss/vgc-battle-logs@3dd2a32dd7867fd8cef95a433073b715230caeb6/logs-gen9vgc2023regd.json.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3496, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2257, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 503, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 350, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 186, in _generate_tables
                  raise ValueError(
              ValueError: Failed to convert pandas DataFrame to Arrow Table from file hf://datasets/cameronangliss/vgc-battle-logs@3dd2a32dd7867fd8cef95a433073b715230caeb6/logs-gen9vgc2023regd.json.

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Dataset from the paper VGC-Bench: Towards Mastering Diverse Team Strategies in Competitive Pokémon.

Code: vgc-bench

Contains data for 16 Gen 9 Pokémon VGC formats, all filtered for Open Team Sheets (OTS). See:

  • scrape_logs.py for the code that scraped these 705,487 logs from the Pokémon Showdown replay database
  • logs2trajs.py for the code that reads the logs from player 1 and 2's perspective, yielding 1,410,934 trajectories (99.997% successful log read rate), or 13,108,147 transitions
  • pretrain.py for the code that uses those ~13M transitions to train a policy with behavior cloning

Each provided json file contains data in the following format:

{
    "<battle-id>": ("<time-of-battle-in-epoch-seconds>", "<battle-log>"),
    ...
}
VGC Format Latest Log (GMT) Total Logs Unrated 1000-1099 1100-1199 1200-1299 1300-1399 1400-1499 1500-1599 1600-1699 1700-1799 1800+
Reg D (2023) 12-31-2025, 23:59:45 561 1,122 0 0 0 0 0 0 0 0 0
Reg F (2024) 05-28-2024, 23:16:45 16,445 32,890 0 0 0 0 0 0 0 0 0
Reg F BO3 (2024) 05-27-2024, 16:29:21 66 132 0 0 0 0 0 0 0 0 0
Reg G (2024) 01-09-2026, 03:41:00 21,730 43,460 0 0 0 0 0 0 0 0 0
Reg G BO3 (2024) 09-01-2024, 00:58:10 58 116 0 0 0 0 0 0 0 0 0
Reg H (2024) 01-01-2025, 17:44:04 16,020 31,520 44 117 88 97 89 59 16 9 1
Reg H BO3 (2024) 12-15-2024, 20:02:50 43 86 0 0 0 0 0 0 0 0 0
Reg G (2025) 05-01-2025, 16:16:30 16,451 19,350 1,889 3,842 2,832 2,691 1,478 589 193 36 2
Reg G BO3 (2025) 05-01-2025, 16:43:52 191,016 41,484 78,396 111,466 72,678 48,962 21,916 6,223 887 20 0
Reg H (2025) 12-02-2025, 04:11:56 7,865 8,936 1,135 1,832 1,259 1,277 825 386 74 6 0
Reg H BO3 (2025) 12-02-2025, 06:13:04 154,800 31,774 76,088 94,643 56,144 33,921 13,209 3,382 392 47 0
Reg I (2025) 01-05-2026, 18:41:24 16,268 19,786 1,988 3,455 2,624 2,360 1,458 669 179 15 2
Reg I BO3 (2025) 09-01-2025, 15:21:06 183,267 43,314 81,082 110,912 68,438 42,764 15,506 3,789 633 96 0
Reg J BO3 (2025) 01-06-2026, 02:46:38 8,051 16,102 0 0 0 0 0 0 0 0 0
Reg F (2026) 01-09-2026, 07:26:28 5,446 7,794 698 793 569 523 338 130 41 6 0
Reg F BO3 (2026) 01-09-2026, 07:53:55 67,400 15,618 35,999 42,738 24,440 12,359 3,102 493 50 1 0

Cite us 

@inproceedings{anglissvgc,
  title={VGC-Bench: Towards Mastering Diverse Team Strategies in Competitive Pok{\'e}mon},
  author={Angliss, Cameron L and Cui, Jiaxun and Hu, Jiaheng and Rahman, Arrasy and Stone, Peter},
  booktitle={The 25th International Conference on Autonomous Agents and Multi-Agent Systems}
}
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