- Deskripsi :
D4RL adalah benchmark sumber terbuka untuk pembelajaran penguatan offline. Ini menyediakan lingkungan dan kumpulan data standar untuk pelatihan dan algoritma pembandingan.
Kumpulan data mengikuti format RLDS untuk mewakili langkah dan episode.
Beranda : https://sites.google.com/view/d4rl/home
Kode sumber :
tfds.d4rl.d4rl_adroit_pen.D4rlAdroitPen
Versi :
-
1.0.0
: Rilis awal. -
1.1.0
(default): Menambahkan is_last.
-
Kunci yang diawasi (Lihat
as_supervised
doc ):None
Gambar ( tfds.show_examples ): Tidak didukung.
Kutipan :
@misc{fu2020d4rl,
title={D4RL: Datasets for Deep Data-Driven Reinforcement Learning},
author={Justin Fu and Aviral Kumar and Ofir Nachum and George Tucker and Sergey Levine},
year={2020},
eprint={2004.07219},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
d4rl_adroit_pen/v0-human (konfigurasi default)
Deskripsi konfigurasi : Lihat detail selengkapnya tentang tugas dan versinya di https://github.com/rail-berkeley/d4rl/wiki/Tasks#adroit
Ukuran unduhan :
1.94 MiB
Ukuran dataset :
2.52 MiB
Di-cache otomatis ( dokumentasi ): Ya
Perpecahan :
Membelah | Contoh |
---|---|
'train' | 50 |
- Struktur fitur :
FeaturesDict({
'steps': Dataset({
'action': Tensor(shape=(24,), dtype=float32),
'discount': float32,
'infos': FeaturesDict({
'qpos': Tensor(shape=(30,), dtype=float32),
'qvel': Tensor(shape=(30,), dtype=float32),
}),
'is_first': bool,
'is_last': bool,
'is_terminal': bool,
'observation': Tensor(shape=(45,), dtype=float32),
'reward': float32,
}),
})
- Dokumentasi fitur :
Fitur | Kelas | Membentuk | Dtype | Keterangan |
---|---|---|---|---|
fiturDict | ||||
Langkah | Himpunan data | |||
langkah/tindakan | Tensor | (24,) | float32 | |
langkah/diskon | Tensor | float32 | ||
langkah/info | fiturDict | |||
langkah/info/qpos | Tensor | (30,) | float32 | |
langkah/info/qvel | Tensor | (30,) | float32 | |
langkah/adalah_pertama | Tensor | bool | ||
langkah/is_last | Tensor | bool | ||
langkah/is_terminal | Tensor | bool | ||
langkah/pengamatan | Tensor | (45,) | float32 | |
langkah/hadiah | Tensor | float32 |
- Contoh ( tfds.as_dataframe ):
d4rl_adroit_pen/v0-kloning
Deskripsi konfigurasi : Lihat detail selengkapnya tentang tugas dan versinya di https://github.com/rail-berkeley/d4rl/wiki/Tasks#adroit
Ukuran unduhan :
292.85 MiB
Ukuran dataset :
252.55 MiB
Di-cache otomatis ( dokumentasi ): Tidak
Perpecahan :
Membelah | Contoh |
---|---|
'train' | 5.023 |
- Struktur fitur :
FeaturesDict({
'steps': Dataset({
'action': Tensor(shape=(24,), dtype=float32),
'discount': float64,
'infos': FeaturesDict({
'qpos': Tensor(shape=(30,), dtype=float64),
'qvel': Tensor(shape=(30,), dtype=float64),
}),
'is_first': bool,
'is_last': bool,
'is_terminal': bool,
'observation': Tensor(shape=(45,), dtype=float64),
'reward': float64,
}),
})
- Dokumentasi fitur :
Fitur | Kelas | Membentuk | Dtype | Keterangan |
---|---|---|---|---|
fiturDict | ||||
Langkah | Himpunan data | |||
langkah/tindakan | Tensor | (24,) | float32 | |
langkah/diskon | Tensor | float64 | ||
langkah/info | fiturDict | |||
langkah/info/qpos | Tensor | (30,) | float64 | |
langkah/info/qvel | Tensor | (30,) | float64 | |
langkah/adalah_pertama | Tensor | bool | ||
langkah/is_last | Tensor | bool | ||
langkah/is_terminal | Tensor | bool | ||
langkah/pengamatan | Tensor | (45,) | float64 | |
langkah/hadiah | Tensor | float64 |
- Contoh ( tfds.as_dataframe ):
d4rl_adroit_pen/v0-expert
Deskripsi konfigurasi : Lihat detail selengkapnya tentang tugas dan versinya di https://github.com/rail-berkeley/d4rl/wiki/Tasks#adroit
Ukuran unduhan :
250.13 MiB
Ukuran dataset :
344.41 MiB
Di-cache otomatis ( dokumentasi ): Tidak
Perpecahan :
Membelah | Contoh |
---|---|
'train' | 5.000 |
- Struktur fitur :
FeaturesDict({
'steps': Dataset({
'action': Tensor(shape=(24,), dtype=float32),
'discount': float32,
'infos': FeaturesDict({
'action_logstd': Tensor(shape=(24,), dtype=float32),
'action_mean': Tensor(shape=(24,), dtype=float32),
'qpos': Tensor(shape=(30,), dtype=float32),
'qvel': Tensor(shape=(30,), dtype=float32),
}),
'is_first': bool,
'is_last': bool,
'is_terminal': bool,
'observation': Tensor(shape=(45,), dtype=float32),
'reward': float32,
}),
})
- Dokumentasi fitur :
Fitur | Kelas | Membentuk | Dtype | Keterangan |
---|---|---|---|---|
fiturDict | ||||
Langkah | Himpunan data | |||
langkah/tindakan | Tensor | (24,) | float32 | |
langkah/diskon | Tensor | float32 | ||
langkah/info | fiturDict | |||
langkah/info/action_logstd | Tensor | (24,) | float32 | |
langkah/info/action_mean | Tensor | (24,) | float32 | |
langkah/info/qpos | Tensor | (30,) | float32 | |
langkah/info/qvel | Tensor | (30,) | float32 | |
langkah/adalah_pertama | Tensor | bool | ||
langkah/is_last | Tensor | bool | ||
langkah/is_terminal | Tensor | bool | ||
langkah/pengamatan | Tensor | (45,) | float32 | |
langkah/hadiah | Tensor | float32 |
- Contoh ( tfds.as_dataframe ):
d4rl_adroit_pen/v1-manusia
Deskripsi konfigurasi : Lihat detail selengkapnya tentang tugas dan versinya di https://github.com/rail-berkeley/d4rl/wiki/Tasks#adroit
Ukuran unduhan :
1.95 MiB
Ukuran dataset :
2.60 MiB
Di-cache otomatis ( dokumentasi ): Ya
Perpecahan :
Membelah | Contoh |
---|---|
'train' | 25 |
- Struktur fitur :
FeaturesDict({
'steps': Dataset({
'action': Tensor(shape=(24,), dtype=float32),
'discount': float32,
'infos': FeaturesDict({
'desired_orien': Tensor(shape=(4,), dtype=float32),
'qpos': Tensor(shape=(30,), dtype=float32),
'qvel': Tensor(shape=(30,), dtype=float32),
}),
'is_first': bool,
'is_last': bool,
'is_terminal': bool,
'observation': Tensor(shape=(45,), dtype=float32),
'reward': float32,
}),
})
- Dokumentasi fitur :
Fitur | Kelas | Membentuk | Dtype | Keterangan |
---|---|---|---|---|
fiturDict | ||||
Langkah | Himpunan data | |||
langkah/tindakan | Tensor | (24,) | float32 | |
langkah/diskon | Tensor | float32 | ||
langkah/info | fiturDict | |||
langkah/info/desired_orien | Tensor | (4,) | float32 | |
langkah/info/qpos | Tensor | (30,) | float32 | |
langkah/info/qvel | Tensor | (30,) | float32 | |
langkah/adalah_pertama | Tensor | bool | ||
langkah/is_last | Tensor | bool | ||
langkah/is_terminal | Tensor | bool | ||
langkah/pengamatan | Tensor | (45,) | float32 | |
langkah/hadiah | Tensor | float32 |
- Contoh ( tfds.as_dataframe ):
d4rl_adroit_pen/v1-digandakan
Deskripsi konfigurasi : Lihat detail selengkapnya tentang tugas dan versinya di https://github.com/rail-berkeley/d4rl/wiki/Tasks#adroit
Ukuran unduhan :
147.89 MiB
Ukuran dataset :
1.43 GiB
Di-cache otomatis ( dokumentasi ): Tidak
Perpecahan :
Membelah | Contoh |
---|---|
'train' | 3.755 |
- Struktur fitur :
FeaturesDict({
'algorithm': string,
'policy': FeaturesDict({
'fc0': FeaturesDict({
'bias': Tensor(shape=(256,), dtype=float32),
'weight': Tensor(shape=(45, 256), dtype=float32),
}),
'fc1': FeaturesDict({
'bias': Tensor(shape=(256,), dtype=float32),
'weight': Tensor(shape=(256, 256), dtype=float32),
}),
'last_fc': FeaturesDict({
'bias': Tensor(shape=(24,), dtype=float32),
'weight': Tensor(shape=(256, 24), dtype=float32),
}),
'nonlinearity': string,
'output_distribution': string,
}),
'steps': Dataset({
'action': Tensor(shape=(24,), dtype=float32),
'discount': float32,
'infos': FeaturesDict({
'desired_orien': Tensor(shape=(4,), dtype=float32),
'qpos': Tensor(shape=(30,), dtype=float32),
'qvel': Tensor(shape=(30,), dtype=float32),
}),
'is_first': bool,
'is_last': bool,
'is_terminal': bool,
'observation': Tensor(shape=(45,), dtype=float32),
'reward': float32,
}),
})
- Dokumentasi fitur :
Fitur | Kelas | Membentuk | Dtype | Keterangan |
---|---|---|---|---|
fiturDict | ||||
algoritma | Tensor | rangkaian | ||
aturan | fiturDict | |||
kebijakan/fc0 | fiturDict | |||
kebijakan/fc0/bias | Tensor | (256,) | float32 | |
kebijakan/fc0/bobot | Tensor | (45, 256) | float32 | |
kebijakan/fc1 | fiturDict | |||
kebijakan/fc1/bias | Tensor | (256,) | float32 | |
kebijakan/fc1/berat | Tensor | (256, 256) | float32 | |
kebijakan/last_fc | fiturDict | |||
kebijakan/last_fc/bias | Tensor | (24,) | float32 | |
kebijakan/last_fc/weight | Tensor | (256, 24) | float32 | |
kebijakan/nonlinier | Tensor | rangkaian | ||
kebijakan/output_distribution | Tensor | rangkaian | ||
Langkah | Himpunan data | |||
langkah/tindakan | Tensor | (24,) | float32 | |
langkah/diskon | Tensor | float32 | ||
langkah/info | fiturDict | |||
langkah/info/desired_orien | Tensor | (4,) | float32 | |
langkah/info/qpos | Tensor | (30,) | float32 | |
langkah/info/qvel | Tensor | (30,) | float32 | |
langkah/adalah_pertama | Tensor | bool | ||
langkah/is_last | Tensor | bool | ||
langkah/is_terminal | Tensor | bool | ||
langkah/pengamatan | Tensor | (45,) | float32 | |
langkah/hadiah | Tensor | float32 |
- Contoh ( tfds.as_dataframe ):
d4rl_adroit_pen/v1-expert
Ukuran unduhan :
249.90 MiB
Ukuran dataset :
548.47 MiB
Di-cache otomatis ( dokumentasi ): Tidak
Perpecahan :
Membelah | Contoh |
---|---|
'train' | 5.000 |
- Struktur fitur :
FeaturesDict({
'algorithm': string,
'policy': FeaturesDict({
'fc0': FeaturesDict({
'bias': Tensor(shape=(64,), dtype=float32),
'weight': Tensor(shape=(64, 45), dtype=float32),
}),
'fc1': FeaturesDict({
'bias': Tensor(shape=(64,), dtype=float32),
'weight': Tensor(shape=(64, 64), dtype=float32),
}),
'last_fc': FeaturesDict({
'bias': Tensor(shape=(24,), dtype=float32),
'weight': Tensor(shape=(24, 64), dtype=float32),
}),
'last_fc_log_std': FeaturesDict({
'bias': Tensor(shape=(24,), dtype=float32),
'weight': Tensor(shape=(24, 64), dtype=float32),
}),
'nonlinearity': string,
'output_distribution': string,
}),
'steps': Dataset({
'action': Tensor(shape=(24,), dtype=float32),
'discount': float32,
'infos': FeaturesDict({
'action_log_std': Tensor(shape=(24,), dtype=float32),
'action_mean': Tensor(shape=(24,), dtype=float32),
'desired_orien': Tensor(shape=(4,), dtype=float32),
'qpos': Tensor(shape=(30,), dtype=float32),
'qvel': Tensor(shape=(30,), dtype=float32),
}),
'is_first': bool,
'is_last': bool,
'is_terminal': bool,
'observation': Tensor(shape=(45,), dtype=float32),
'reward': float32,
}),
})
- Dokumentasi fitur :
Fitur | Kelas | Membentuk | Dtype | Keterangan |
---|---|---|---|---|
fiturDict | ||||
algoritma | Tensor | rangkaian | ||
aturan | fiturDict | |||
kebijakan/fc0 | fiturDict | |||
kebijakan/fc0/bias | Tensor | (64,) | float32 | |
kebijakan/fc0/bobot | Tensor | (64, 45) | float32 | |
kebijakan/fc1 | fiturDict | |||
kebijakan/fc1/bias | Tensor | (64,) | float32 | |
kebijakan/fc1/berat | Tensor | (64, 64) | float32 | |
kebijakan/last_fc | fiturDict | |||
kebijakan/last_fc/bias | Tensor | (24,) | float32 | |
kebijakan/last_fc/weight | Tensor | (24, 64) | float32 | |
kebijakan/last_fc_log_std | fiturDict | |||
kebijakan/last_fc_log_std/bias | Tensor | (24,) | float32 | |
kebijakan/last_fc_log_std/weight | Tensor | (24, 64) | float32 | |
kebijakan/nonlinier | Tensor | rangkaian | ||
kebijakan/output_distribution | Tensor | rangkaian | ||
Langkah | Himpunan data | |||
langkah/tindakan | Tensor | (24,) | float32 | |
langkah/diskon | Tensor | float32 | ||
langkah/info | fiturDict | |||
langkah/info/action_log_std | Tensor | (24,) | float32 | |
langkah/info/action_mean | Tensor | (24,) | float32 | |
langkah/info/desired_orien | Tensor | (4,) | float32 | |
langkah/info/qpos | Tensor | (30,) | float32 | |
langkah/info/qvel | Tensor | (30,) | float32 | |
langkah/adalah_pertama | Tensor | bool | ||
langkah/is_last | Tensor | bool | ||
langkah/is_terminal | Tensor | bool | ||
langkah/pengamatan | Tensor | (45,) | float32 | |
langkah/hadiah | Tensor | float32 |
- Contoh ( tfds.as_dataframe ):