d4rl_mujoco_walker2d

• Keterangan :

D4RL adalah tolok ukur sumber terbuka untuk pembelajaran penguatan offline. Ini menyediakan lingkungan dan kumpulan data standar untuk pelatihan dan algoritma benchmarking.

Kumpulan data mengikuti format RLDS untuk mewakili langkah dan episode.

• Dokumentasi Tambahan : Jelajahi Makalah Dengan Kode north_east

• Deskripsi konfigurasi : Lihat detail lebih lanjut tentang tugas dan versinya di https://github.com/rail-berkeley/d4rl/wiki/Tasks#gym

• Beranda : https://sites.google.com/view/d4rl-anonymous

• Kode sumber : tfds.d4rl.d4rl_mujoco_walker2d.D4rlMujocoWalker2d

• Versi :

  • 1.0.0 : Rilis awal.
  • 1.1.0 : Ditambahkan is_last.
  • 1.2.0 (default): Diperbarui untuk memperhitungkan observasi berikutnya.

• Kunci yang diawasi (Lihat dokumen as_supervised ): 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_mujoco_walker2d/v0-expert (konfigurasi default)

• Ukuran unduhan : 78.41 MiB

• Ukuran kumpulan data : 98.64 MiB

• Cache otomatis ( dokumentasi ): Ya

• Perpecahan :

• Struktur fitur :

FeaturesDict({
    'steps': Dataset({
        'action': Tensor(shape=(6,), dtype=float32),
        'discount': float32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(17,), dtype=float32),
        'reward': float32,
    }),
})

• Dokumentasi fitur :

• Contoh ( tfds.as_dataframe ):

d4rl_mujoco_walker2d/v0-medium

• Ukuran unduhan : 80.83 MiB

• Ukuran kumpulan data : 99.72 MiB

• Cache otomatis ( dokumentasi ): Ya

• Perpecahan :

• Struktur fitur :

FeaturesDict({
    'steps': Dataset({
        'action': Tensor(shape=(6,), dtype=float32),
        'discount': float32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(17,), dtype=float32),
        'reward': float32,
    }),
})

• Dokumentasi fitur :

• Contoh ( tfds.as_dataframe ):

d4rl_mujoco_walker2d/v0-medium-expert

• Ukuran unduhan : 159.24 MiB

• Ukuran kumpulan data : 198.36 MiB

• Cache otomatis ( dokumentasi ): Hanya ketika shuffle_files=False (kereta)

• Perpecahan :

• Struktur fitur :

FeaturesDict({
    'steps': Dataset({
        'action': Tensor(shape=(6,), dtype=float32),
        'discount': float32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(17,), dtype=float32),
        'reward': float32,
    }),
})

• Dokumentasi fitur :

• Contoh ( tfds.as_dataframe ):

d4rl_mujoco_walker2d/v0-campuran

• Ukuran unduhan : 8.42 MiB

• Ukuran kumpulan data : 10.06 MiB

• Cache otomatis ( dokumentasi ): Ya

• Perpecahan :

• Struktur fitur :

FeaturesDict({
    'steps': Dataset({
        'action': Tensor(shape=(6,), dtype=float32),
        'discount': float32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(17,), dtype=float32),
        'reward': float32,
    }),
})

• Dokumentasi fitur :

• Contoh ( tfds.as_dataframe ):

d4rl_mujoco_walker2d/v0-acak

• Ukuran unduhan : 78.41 MiB

• Ukuran kumpulan data : 112.04 MiB

• Cache otomatis ( dokumentasi ): Ya

• Perpecahan :

• Struktur fitur :

FeaturesDict({
    'steps': Dataset({
        'action': Tensor(shape=(6,), dtype=float32),
        'discount': float32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(17,), dtype=float32),
        'reward': float32,
    }),
})

• Dokumentasi fitur :

• Contoh ( tfds.as_dataframe ):

d4rl_mujoco_walker2d/v1-expert

• Ukuran unduhan : 143.06 MiB

• Ukuran kumpulan data : 452.72 MiB

• Cache otomatis ( dokumentasi ): Tidak

• Perpecahan :

• Struktur fitur :

FeaturesDict({
    'algorithm': string,
    'iteration': int32,
    'policy': FeaturesDict({
        'fc0': FeaturesDict({
            'bias': Tensor(shape=(256,), dtype=float32),
            'weight': Tensor(shape=(256, 17), dtype=float32),
        }),
        'fc1': FeaturesDict({
            'bias': Tensor(shape=(256,), dtype=float32),
            'weight': Tensor(shape=(256, 256), dtype=float32),
        }),
        'last_fc': FeaturesDict({
            'bias': Tensor(shape=(6,), dtype=float32),
            'weight': Tensor(shape=(6, 256), dtype=float32),
        }),
        'last_fc_log_std': FeaturesDict({
            'bias': Tensor(shape=(6,), dtype=float32),
            'weight': Tensor(shape=(6, 256), dtype=float32),
        }),
        'nonlinearity': string,
        'output_distribution': string,
    }),
    'steps': Dataset({
        'action': Tensor(shape=(6,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'action_log_probs': float32,
            'qpos': Tensor(shape=(9,), dtype=float32),
            'qvel': Tensor(shape=(9,), dtype=float32),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(17,), dtype=float32),
        'reward': float32,
    }),
})

• Dokumentasi fitur :

• Contoh ( tfds.as_dataframe ):

d4rl_mujoco_walker2d/v1-medium

• Ukuran unduhan : 144.23 MiB

• Ukuran kumpulan data : 510.08 MiB

• Cache otomatis ( dokumentasi ): Tidak

• Perpecahan :

• Struktur fitur :

FeaturesDict({
    'algorithm': string,
    'iteration': int32,
    'policy': FeaturesDict({
        'fc0': FeaturesDict({
            'bias': Tensor(shape=(256,), dtype=float32),
            'weight': Tensor(shape=(256, 17), dtype=float32),
        }),
        'fc1': FeaturesDict({
            'bias': Tensor(shape=(256,), dtype=float32),
            'weight': Tensor(shape=(256, 256), dtype=float32),
        }),
        'last_fc': FeaturesDict({
            'bias': Tensor(shape=(6,), dtype=float32),
            'weight': Tensor(shape=(6, 256), dtype=float32),
        }),
        'last_fc_log_std': FeaturesDict({
            'bias': Tensor(shape=(6,), dtype=float32),
            'weight': Tensor(shape=(6, 256), dtype=float32),
        }),
        'nonlinearity': string,
        'output_distribution': string,
    }),
    'steps': Dataset({
        'action': Tensor(shape=(6,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'action_log_probs': float32,
            'qpos': Tensor(shape=(9,), dtype=float32),
            'qvel': Tensor(shape=(9,), dtype=float32),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(17,), dtype=float32),
        'reward': float32,
    }),
})

• Dokumentasi fitur :

• Contoh ( tfds.as_dataframe ):

d4rl_mujoco_walker2d/v1-medium-expert

• Ukuran unduhan : 286.69 MiB

• Ukuran kumpulan data : 342.46 MiB

• Cache otomatis ( dokumentasi ): Tidak

• Perpecahan :

• Struktur fitur :

FeaturesDict({
    'steps': Dataset({
        'action': Tensor(shape=(6,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'action_log_probs': float32,
            'qpos': Tensor(shape=(9,), dtype=float32),
            'qvel': Tensor(shape=(9,), dtype=float32),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(17,), dtype=float32),
        'reward': float32,
    }),
})

• Dokumentasi fitur :

• Contoh ( tfds.as_dataframe ):

d4rl_mujoco_walker2d/v1-medium-replay

• Ukuran unduhan : 84.37 MiB

• Ukuran kumpulan data : 52.10 MiB

• Cache otomatis ( dokumentasi ): Ya

• Perpecahan :

• Struktur fitur :

FeaturesDict({
    'algorithm': string,
    'iteration': int32,
    'steps': Dataset({
        'action': Tensor(shape=(6,), dtype=float64),
        'discount': float64,
        'infos': FeaturesDict({
            'action_log_probs': float64,
            'qpos': Tensor(shape=(9,), dtype=float64),
            'qvel': Tensor(shape=(9,), dtype=float64),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(17,), dtype=float64),
        'reward': float64,
    }),
})

• Dokumentasi fitur :

• Contoh ( tfds.as_dataframe ):

d4rl_mujoco_walker2d/v1-putar ulang penuh

• Ukuran unduhan : 278.95 MiB

• Ukuran kumpulan data : 171.66 MiB

• Cache otomatis ( dokumentasi ): Hanya ketika shuffle_files=False (kereta)

• Perpecahan :

• Struktur fitur :

FeaturesDict({
    'algorithm': string,
    'iteration': int32,
    'steps': Dataset({
        'action': Tensor(shape=(6,), dtype=float64),
        'discount': float64,
        'infos': FeaturesDict({
            'action_log_probs': float64,
            'qpos': Tensor(shape=(9,), dtype=float64),
            'qvel': Tensor(shape=(9,), dtype=float64),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(17,), dtype=float64),
        'reward': float64,
    }),
})

• Dokumentasi fitur :

• Contoh ( tfds.as_dataframe ):

d4rl_mujoco_walker2d/v1-acak

• Ukuran unduhan : 132.36 MiB

• Ukuran kumpulan data : 192.06 MiB

• Cache otomatis ( dokumentasi ): Hanya ketika shuffle_files=False (kereta)

• Perpecahan :

• Struktur fitur :

FeaturesDict({
    'steps': Dataset({
        'action': Tensor(shape=(6,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'action_log_probs': float32,
            'qpos': Tensor(shape=(9,), dtype=float32),
            'qvel': Tensor(shape=(9,), dtype=float32),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(17,), dtype=float32),
        'reward': float32,
    }),
})

• Dokumentasi fitur :

• Contoh ( tfds.as_dataframe ):

d4rl_mujoco_walker2d/v2-expert

• Ukuran unduhan : 219.89 MiB

• Ukuran kumpulan data : 452.16 MiB

• Cache otomatis ( dokumentasi ): Tidak

• Perpecahan :

• Struktur fitur :

FeaturesDict({
    'algorithm': string,
    'iteration': int32,
    'policy': FeaturesDict({
        'fc0': FeaturesDict({
            'bias': Tensor(shape=(256,), dtype=float32),
            'weight': Tensor(shape=(256, 17), dtype=float32),
        }),
        'fc1': FeaturesDict({
            'bias': Tensor(shape=(256,), dtype=float32),
            'weight': Tensor(shape=(256, 256), dtype=float32),
        }),
        'last_fc': FeaturesDict({
            'bias': Tensor(shape=(6,), dtype=float32),
            'weight': Tensor(shape=(6, 256), dtype=float32),
        }),
        'last_fc_log_std': FeaturesDict({
            'bias': Tensor(shape=(6,), dtype=float32),
            'weight': Tensor(shape=(6, 256), dtype=float32),
        }),
        'nonlinearity': string,
        'output_distribution': string,
    }),
    'steps': Dataset({
        'action': Tensor(shape=(6,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'action_log_probs': float64,
            'qpos': Tensor(shape=(9,), dtype=float64),
            'qvel': Tensor(shape=(9,), dtype=float64),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(17,), dtype=float32),
        'reward': float32,
    }),
})

• Dokumentasi fitur :

• Contoh ( tfds.as_dataframe ):

d4rl_mujoco_walker2d/v2-putar ulang penuh

• Ukuran unduhan : 271.91 MiB

• Ukuran kumpulan data : 171.66 MiB

• Cache otomatis ( dokumentasi ): Hanya ketika shuffle_files=False (kereta)

• Perpecahan :

• Struktur fitur :

FeaturesDict({
    'algorithm': string,
    'iteration': int32,
    'steps': Dataset({
        'action': Tensor(shape=(6,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'action_log_probs': float64,
            'qpos': Tensor(shape=(9,), dtype=float64),
            'qvel': Tensor(shape=(9,), dtype=float64),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(17,), dtype=float32),
        'reward': float32,
    }),
})

• Dokumentasi fitur :

• Contoh ( tfds.as_dataframe ):

d4rl_mujoco_walker2d/v2-medium

• Ukuran unduhan : 221.50 MiB

• Ukuran kumpulan data : 505.58 MiB

• Cache otomatis ( dokumentasi ): Tidak

• Perpecahan :

• Struktur fitur :

FeaturesDict({
    'algorithm': string,
    'iteration': int32,
    'policy': FeaturesDict({
        'fc0': FeaturesDict({
            'bias': Tensor(shape=(256,), dtype=float32),
            'weight': Tensor(shape=(256, 17), dtype=float32),
        }),
        'fc1': FeaturesDict({
            'bias': Tensor(shape=(256,), dtype=float32),
            'weight': Tensor(shape=(256, 256), dtype=float32),
        }),
        'last_fc': FeaturesDict({
            'bias': Tensor(shape=(6,), dtype=float32),
            'weight': Tensor(shape=(6, 256), dtype=float32),
        }),
        'last_fc_log_std': FeaturesDict({
            'bias': Tensor(shape=(6,), dtype=float32),
            'weight': Tensor(shape=(6, 256), dtype=float32),
        }),
        'nonlinearity': string,
        'output_distribution': string,
    }),
    'steps': Dataset({
        'action': Tensor(shape=(6,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'action_log_probs': float64,
            'qpos': Tensor(shape=(9,), dtype=float64),
            'qvel': Tensor(shape=(9,), dtype=float64),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(17,), dtype=float32),
        'reward': float32,
    }),
})

• Dokumentasi fitur :

• Contoh ( tfds.as_dataframe ):

d4rl_mujoco_walker2d/v2-medium-expert

• Ukuran unduhan : 440.79 MiB

• Ukuran kumpulan data : 342.45 MiB

• Cache otomatis ( dokumentasi ): Tidak

• Perpecahan :

• Struktur fitur :

FeaturesDict({
    'steps': Dataset({
        'action': Tensor(shape=(6,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'action_log_probs': float64,
            'qpos': Tensor(shape=(9,), dtype=float64),
            'qvel': Tensor(shape=(9,), dtype=float64),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(17,), dtype=float32),
        'reward': float32,
    }),
})

• Dokumentasi fitur :

• Contoh ( tfds.as_dataframe ):

d4rl_mujoco_walker2d/v2-medium-replay

• Ukuran unduhan : 82.32 MiB

• Ukuran kumpulan data : 52.10 MiB

• Cache otomatis ( dokumentasi ): Ya

• Perpecahan :

• Struktur fitur :

FeaturesDict({
    'algorithm': string,
    'iteration': int32,
    'steps': Dataset({
        'action': Tensor(shape=(6,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'action_log_probs': float64,
            'qpos': Tensor(shape=(9,), dtype=float64),
            'qvel': Tensor(shape=(9,), dtype=float64),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(17,), dtype=float32),
        'reward': float32,
    }),
})

• Dokumentasi fitur :

• Contoh ( tfds.as_dataframe ):

d4rl_mujoco_walker2d/v2-acak

• Ukuran unduhan : 206.10 MiB

• Ukuran kumpulan data : 192.11 MiB

• Cache otomatis ( dokumentasi ): Hanya ketika shuffle_files=False (kereta)

• Perpecahan :

• Struktur fitur :

FeaturesDict({
    'steps': Dataset({
        'action': Tensor(shape=(6,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'action_log_probs': float64,
            'qpos': Tensor(shape=(9,), dtype=float64),
            'qvel': Tensor(shape=(9,), dtype=float64),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(17,), dtype=float32),
        'reward': float32,
    }),
})

• Dokumentasi fitur :

• Contoh ( tfds.as_dataframe ):