d4rl_adroit_hammer

  • Description:

D4RL is an open-source benchmark for offline reinforcement learning. It provides standardized environments and datasets for training and benchmarking algorithms.

The datasets follow the RLDS format to represent steps and episodes.

@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_hammer/v0-human (default config)

  • Download size: 5.33 MiB

  • Dataset size: 6.10 MiB

  • Auto-cached (documentation): Yes

  • Splits:

Split Examples
'train' 70
  • Feature structure:
FeaturesDict({
    'steps': Dataset({
        'action': Tensor(shape=(26,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'qpos': Tensor(shape=(33,), dtype=float32),
            'qvel': Tensor(shape=(33,), dtype=float32),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(46,), dtype=float32),
        'reward': float32,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
steps Dataset
steps/action Tensor (26,) float32
steps/discount Tensor float32
steps/infos FeaturesDict
steps/infos/qpos Tensor (33,) float32
steps/infos/qvel Tensor (33,) float32
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (46,) float32
steps/reward Tensor float32

d4rl_adroit_hammer/v0-cloned

  • Download size: 644.69 MiB

  • Dataset size: 538.97 MiB

  • Auto-cached (documentation): No

  • Splits:

Split Examples
'train' 5,594
  • Feature structure:
FeaturesDict({
    'steps': Dataset({
        'action': Tensor(shape=(26,), dtype=float32),
        'discount': float64,
        'infos': FeaturesDict({
            'qpos': Tensor(shape=(33,), dtype=float64),
            'qvel': Tensor(shape=(33,), dtype=float64),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(46,), dtype=float64),
        'reward': float64,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
steps Dataset
steps/action Tensor (26,) float32
steps/discount Tensor float64
steps/infos FeaturesDict
steps/infos/qpos Tensor (33,) float64
steps/infos/qvel Tensor (33,) float64
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (46,) float64
steps/reward Tensor float64

d4rl_adroit_hammer/v0-expert

  • Download size: 529.91 MiB

  • Dataset size: 737.00 MiB

  • Auto-cached (documentation): No

  • Splits:

Split Examples
'train' 5,000
  • Feature structure:
FeaturesDict({
    'steps': Dataset({
        'action': Tensor(shape=(26,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'action_logstd': Tensor(shape=(26,), dtype=float32),
            'action_mean': Tensor(shape=(26,), dtype=float32),
            'qpos': Tensor(shape=(33,), dtype=float32),
            'qvel': Tensor(shape=(33,), dtype=float32),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(46,), dtype=float32),
        'reward': float32,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
steps Dataset
steps/action Tensor (26,) float32
steps/discount Tensor float32
steps/infos FeaturesDict
steps/infos/action_logstd Tensor (26,) float32
steps/infos/action_mean Tensor (26,) float32
steps/infos/qpos Tensor (33,) float32
steps/infos/qvel Tensor (33,) float32
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (46,) float32
steps/reward Tensor float32

d4rl_adroit_hammer/v1-human

  • Download size: 5.35 MiB

  • Dataset size: 6.34 MiB

  • Auto-cached (documentation): Yes

  • Splits:

Split Examples
'train' 25
  • Feature structure:
FeaturesDict({
    'steps': Dataset({
        'action': Tensor(shape=(26,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'board_pos': Tensor(shape=(3,), dtype=float32),
            'qpos': Tensor(shape=(33,), dtype=float32),
            'qvel': Tensor(shape=(33,), dtype=float32),
            'target_pos': Tensor(shape=(3,), dtype=float32),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(46,), dtype=float32),
        'reward': float32,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
steps Dataset
steps/action Tensor (26,) float32
steps/discount Tensor float32
steps/infos FeaturesDict
steps/infos/board_pos Tensor (3,) float32
steps/infos/qpos Tensor (33,) float32
steps/infos/qvel Tensor (33,) float32
steps/infos/target_pos Tensor (3,) float32
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (46,) float32
steps/reward Tensor float32

d4rl_adroit_hammer/v1-cloned

  • Download size: 425.93 MiB

  • Dataset size: 1.68 GiB

  • Auto-cached (documentation): No

  • Splits:

Split Examples
'train' 3,606
  • Feature structure:
FeaturesDict({
    'algorithm': string,
    'policy': FeaturesDict({
        'fc0': FeaturesDict({
            'bias': Tensor(shape=(256,), dtype=float32),
            'weight': Tensor(shape=(46, 256), dtype=float32),
        }),
        'fc1': FeaturesDict({
            'bias': Tensor(shape=(256,), dtype=float32),
            'weight': Tensor(shape=(256, 256), dtype=float32),
        }),
        'last_fc': FeaturesDict({
            'bias': Tensor(shape=(26,), dtype=float32),
            'weight': Tensor(shape=(256, 26), dtype=float32),
        }),
        'nonlinearity': string,
        'output_distribution': string,
    }),
    'steps': Dataset({
        'action': Tensor(shape=(26,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'board_pos': Tensor(shape=(3,), dtype=float32),
            'qpos': Tensor(shape=(33,), dtype=float32),
            'qvel': Tensor(shape=(33,), dtype=float32),
            'target_pos': Tensor(shape=(3,), dtype=float32),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(46,), dtype=float32),
        'reward': float32,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
algorithm Tensor string
policy FeaturesDict
policy/fc0 FeaturesDict
policy/fc0/bias Tensor (256,) float32
policy/fc0/weight Tensor (46, 256) float32
policy/fc1 FeaturesDict
policy/fc1/bias Tensor (256,) float32
policy/fc1/weight Tensor (256, 256) float32
policy/last_fc FeaturesDict
policy/last_fc/bias Tensor (26,) float32
policy/last_fc/weight Tensor (256, 26) float32
policy/nonlinearity Tensor string
policy/output_distribution Tensor string
steps Dataset
steps/action Tensor (26,) float32
steps/discount Tensor float32
steps/infos FeaturesDict
steps/infos/board_pos Tensor (3,) float32
steps/infos/qpos Tensor (33,) float32
steps/infos/qvel Tensor (33,) float32
steps/infos/target_pos Tensor (3,) float32
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (46,) float32
steps/reward Tensor float32

d4rl_adroit_hammer/v1-expert

  • Download size: 531.24 MiB

  • Dataset size: 843.54 MiB

  • Auto-cached (documentation): No

  • Splits:

Split Examples
'train' 5,000
  • Feature structure:
FeaturesDict({
    'algorithm': string,
    'policy': FeaturesDict({
        'fc0': FeaturesDict({
            'bias': Tensor(shape=(32,), dtype=float32),
            'weight': Tensor(shape=(32, 46), dtype=float32),
        }),
        'fc1': FeaturesDict({
            'bias': Tensor(shape=(32,), dtype=float32),
            'weight': Tensor(shape=(32, 32), dtype=float32),
        }),
        'last_fc': FeaturesDict({
            'bias': Tensor(shape=(26,), dtype=float32),
            'weight': Tensor(shape=(26, 32), dtype=float32),
        }),
        'last_fc_log_std': FeaturesDict({
            'bias': Tensor(shape=(26,), dtype=float32),
            'weight': Tensor(shape=(26, 32), dtype=float32),
        }),
        'nonlinearity': string,
        'output_distribution': string,
    }),
    'steps': Dataset({
        'action': Tensor(shape=(26,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'action_log_std': Tensor(shape=(26,), dtype=float32),
            'action_mean': Tensor(shape=(26,), dtype=float32),
            'board_pos': Tensor(shape=(3,), dtype=float32),
            'qpos': Tensor(shape=(33,), dtype=float32),
            'qvel': Tensor(shape=(33,), dtype=float32),
            'target_pos': Tensor(shape=(3,), dtype=float32),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(46,), dtype=float32),
        'reward': float32,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
algorithm Tensor string
policy FeaturesDict
policy/fc0 FeaturesDict
policy/fc0/bias Tensor (32,) float32
policy/fc0/weight Tensor (32, 46) float32
policy/fc1 FeaturesDict
policy/fc1/bias Tensor (32,) float32
policy/fc1/weight Tensor (32, 32) float32
policy/last_fc FeaturesDict
policy/last_fc/bias Tensor (26,) float32
policy/last_fc/weight Tensor (26, 32) float32
policy/last_fc_log_std FeaturesDict
policy/last_fc_log_std/bias Tensor (26,) float32
policy/last_fc_log_std/weight Tensor (26, 32) float32
policy/nonlinearity Tensor string
policy/output_distribution Tensor string
steps Dataset
steps/action Tensor (26,) float32
steps/discount Tensor float32
steps/infos FeaturesDict
steps/infos/action_log_std Tensor (26,) float32
steps/infos/action_mean Tensor (26,) float32
steps/infos/board_pos Tensor (3,) float32
steps/infos/qpos Tensor (33,) float32
steps/infos/qvel Tensor (33,) float32
steps/infos/target_pos Tensor (3,) float32
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (46,) float32
steps/reward Tensor float32