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Datasets:
cameronangliss
/
vgc-battle-logs

Tasks:
Reinforcement Learning
Size:
100K<n<1M
ArXiv:
License:
Dataset card Files
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Dataset Viewer
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@59edabc06a959b3d3521b52c30ca8654cfde2cc6/logs-gen9vgc2023regd.json.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 228, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 3422, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2187, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2391, in iter
                  for key, example in iterator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1882, in __iter__
                  for key, pa_table in self._iter_arrow():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1904, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 499, in _iter_arrow
                  for key, pa_table in iterator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 346, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                File "/src/services/worker/.venv/lib/python3.9/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@59edabc06a959b3d3521b52c30ca8654cfde2cc6/logs-gen9vgc2023regd.json.

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Dataset from the paper VGC-Bench: A Benchmark for Generalizing Across Diverse Team Strategies in Competitive Pokémon.

Code: vgc-bench

Contains data for 14 Gen 9 Pokémon VGC formats, all filtered for Open Team Sheets (OTS). See:

  • scrape_logs.py for the code that scraped these 531,475 logs from the Pokémon Showdown replay database
  • logs2trajs.py for the code that reads the logs from player 1 and 2's perspective, yielding 1,062,821 trajectories (99.99% successful log read rate), or 9,863,801 transitions
  • pretrain.py for the code that uses those ~10M 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) 09-17-2025, 15:53:30 550 1,100 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) 09-20-2025, 02:37:25 20,875 41,750 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) 09-20-2025, 17:51:04 3,238 3,986 588 732 427 383 246 101 12 1 0
Reg H BO3 (2025) 09-20-2025, 18:33:27 65,203 10,568 40,586 43,406 21,091 10,504 3,426 762 60 3 0
Reg I (2025) 09-20-2025, 01:18:35 15,637 18,524 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) 09-20-2025, 18:16:02 2,606 5,212 0 0 0 0 0 0 0 0 0

Cite us 

@article{angliss2025benchmark,
  title={A Benchmark for Generalizing Across Diverse Team Strategies in Competitive Pok$\backslash$'emon},
  author={Angliss, Cameron and Cui, Jiaxun and Hu, Jiaheng and Rahman, Arrasy and Stone, Peter},
  journal={arXiv preprint arXiv:2506.10326},
  year={2025}
}
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