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d4rl_mujoco_hopper

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  • 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_mujoco_hopper/v0-expert (default config)

  • Download size: 51.56 MiB

  • Dataset size: 64.10 MiB

  • Auto-cached (documentation): Yes

  • Splits:

Split Examples
'train' 1,029
  • Feature structure:
FeaturesDict({
    'steps': Dataset({
        'action': Tensor(shape=(3,), dtype=float32),
        'discount': float32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(11,), dtype=float32),
        'reward': float32,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
steps Dataset
steps/action Tensor (3,) float32
steps/discount Tensor float32
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (11,) float32
steps/reward Tensor float32

d4rl_mujoco_hopper/v0-medium

  • Download size: 51.74 MiB

  • Dataset size: 64.68 MiB

  • Auto-cached (documentation): Yes

  • Splits:

Split Examples
'train' 3,064
  • Feature structure:
FeaturesDict({
    'steps': Dataset({
        'action': Tensor(shape=(3,), dtype=float32),
        'discount': float32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(11,), dtype=float32),
        'reward': float32,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
steps Dataset
steps/action Tensor (3,) float32
steps/discount Tensor float32
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (11,) float32
steps/reward Tensor float32

d4rl_mujoco_hopper/v0-medium-expert

  • Download size: 62.01 MiB

  • Dataset size: 77.25 MiB

  • Auto-cached (documentation): Yes

  • Splits:

Split Examples
'train' 2,277
  • Feature structure:
FeaturesDict({
    'steps': Dataset({
        'action': Tensor(shape=(3,), dtype=float32),
        'discount': float32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(11,), dtype=float32),
        'reward': float32,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
steps Dataset
steps/action Tensor (3,) float32
steps/discount Tensor float32
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (11,) float32
steps/reward Tensor float32

d4rl_mujoco_hopper/v0-mixed

  • Download size: 10.48 MiB

  • Dataset size: 13.15 MiB

  • Auto-cached (documentation): Yes

  • Splits:

Split Examples
'train' 1,250
  • Feature structure:
FeaturesDict({
    'steps': Dataset({
        'action': Tensor(shape=(3,), dtype=float32),
        'discount': float32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(11,), dtype=float32),
        'reward': float32,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
steps Dataset
steps/action Tensor (3,) float32
steps/discount Tensor float32
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (11,) float32
steps/reward Tensor float32

d4rl_mujoco_hopper/v0-random

  • Download size: 51.83 MiB

  • Dataset size: 66.06 MiB

  • Auto-cached (documentation): Yes

  • Splits:

Split Examples
'train' 8,793
  • Feature structure:
FeaturesDict({
    'steps': Dataset({
        'action': Tensor(shape=(3,), dtype=float32),
        'discount': float32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(11,), dtype=float32),
        'reward': float32,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
steps Dataset
steps/action Tensor (3,) float32
steps/discount Tensor float32
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (11,) float32
steps/reward Tensor float32

d4rl_mujoco_hopper/v1-expert

  • Download size: 93.19 MiB

  • Dataset size: 608.03 MiB

  • Auto-cached (documentation): No

  • Splits:

Split Examples
'train' 1,836
  • Feature structure:
FeaturesDict({
    'algorithm': object,
    'iteration': int32,
    'policy': FeaturesDict({
        'fc0': FeaturesDict({
            'bias': Tensor(shape=(256,), dtype=float32),
            'weight': Tensor(shape=(256, 11), dtype=float32),
        }),
        'fc1': FeaturesDict({
            'bias': Tensor(shape=(256,), dtype=float32),
            'weight': Tensor(shape=(256, 256), dtype=float32),
        }),
        'last_fc': FeaturesDict({
            'bias': Tensor(shape=(3,), dtype=float32),
            'weight': Tensor(shape=(3, 256), dtype=float32),
        }),
        'last_fc_log_std': FeaturesDict({
            'bias': Tensor(shape=(3,), dtype=float32),
            'weight': Tensor(shape=(3, 256), dtype=float32),
        }),
        'nonlinearity': object,
        'output_distribution': object,
    }),
    'steps': Dataset({
        'action': Tensor(shape=(3,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'action_log_probs': float32,
            'qpos': Tensor(shape=(6,), dtype=float32),
            'qvel': Tensor(shape=(6,), dtype=float32),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(11,), dtype=float32),
        'reward': float32,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
algorithm Tensor object
iteration Tensor int32
policy FeaturesDict
policy/fc0 FeaturesDict
policy/fc0/bias Tensor (256,) float32
policy/fc0/weight Tensor (256, 11) 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 (3,) float32
policy/last_fc/weight Tensor (3, 256) float32
policy/last_fc_log_std FeaturesDict
policy/last_fc_log_std/bias Tensor (3,) float32
policy/last_fc_log_std/weight Tensor (3, 256) float32
policy/nonlinearity Tensor object
policy/output_distribution Tensor object
steps Dataset
steps/action Tensor (3,) float32
steps/discount Tensor float32
steps/infos FeaturesDict
steps/infos/action_log_probs Tensor float32
steps/infos/qpos Tensor (6,) float32
steps/infos/qvel Tensor (6,) float32
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (11,) float32
steps/reward Tensor float32

d4rl_mujoco_hopper/v1-medium

  • Download size: 92.03 MiB

  • Dataset size: 1.78 GiB

  • Auto-cached (documentation): No

  • Splits:

Split Examples
'train' 6,328
  • Feature structure:
FeaturesDict({
    'algorithm': object,
    'iteration': int32,
    'policy': FeaturesDict({
        'fc0': FeaturesDict({
            'bias': Tensor(shape=(256,), dtype=float32),
            'weight': Tensor(shape=(256, 11), dtype=float32),
        }),
        'fc1': FeaturesDict({
            'bias': Tensor(shape=(256,), dtype=float32),
            'weight': Tensor(shape=(256, 256), dtype=float32),
        }),
        'last_fc': FeaturesDict({
            'bias': Tensor(shape=(3,), dtype=float32),
            'weight': Tensor(shape=(3, 256), dtype=float32),
        }),
        'last_fc_log_std': FeaturesDict({
            'bias': Tensor(shape=(3,), dtype=float32),
            'weight': Tensor(shape=(3, 256), dtype=float32),
        }),
        'nonlinearity': object,
        'output_distribution': object,
    }),
    'steps': Dataset({
        'action': Tensor(shape=(3,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'action_log_probs': float32,
            'qpos': Tensor(shape=(6,), dtype=float32),
            'qvel': Tensor(shape=(6,), dtype=float32),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(11,), dtype=float32),
        'reward': float32,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
algorithm Tensor object
iteration Tensor int32
policy FeaturesDict
policy/fc0 FeaturesDict
policy/fc0/bias Tensor (256,) float32
policy/fc0/weight Tensor (256, 11) 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 (3,) float32
policy/last_fc/weight Tensor (3, 256) float32
policy/last_fc_log_std FeaturesDict
policy/last_fc_log_std/bias Tensor (3,) float32
policy/last_fc_log_std/weight Tensor (3, 256) float32
policy/nonlinearity Tensor object
policy/output_distribution Tensor object
steps Dataset
steps/action Tensor (3,) float32
steps/discount Tensor float32
steps/infos FeaturesDict
steps/infos/action_log_probs Tensor float32
steps/infos/qpos Tensor (6,) float32
steps/infos/qvel Tensor (6,) float32
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (11,) float32
steps/reward Tensor float32

d4rl_mujoco_hopper/v1-medium-expert

  • Download size: 184.59 MiB

  • Dataset size: 230.24 MiB

  • Auto-cached (documentation): Only when shuffle_files=False (train)

  • Splits:

Split Examples
'train' 8,163
  • Feature structure:
FeaturesDict({
    'steps': Dataset({
        'action': Tensor(shape=(3,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'action_log_probs': float32,
            'qpos': Tensor(shape=(6,), dtype=float32),
            'qvel': Tensor(shape=(6,), dtype=float32),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(11,), dtype=float32),
        'reward': float32,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
steps Dataset
steps/action Tensor (3,) float32
steps/discount Tensor float32
steps/infos FeaturesDict
steps/infos/action_log_probs Tensor float32
steps/infos/qpos Tensor (6,) float32
steps/infos/qvel Tensor (6,) float32
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (11,) float32
steps/reward Tensor float32

d4rl_mujoco_hopper/v1-medium-replay

  • Download size: 55.65 MiB

  • Dataset size: 34.78 MiB

  • Auto-cached (documentation): Yes

  • Splits:

Split Examples
'train' 1,151
  • Feature structure:
FeaturesDict({
    'algorithm': object,
    'iteration': int32,
    'steps': Dataset({
        'action': Tensor(shape=(3,), dtype=float64),
        'discount': float64,
        'infos': FeaturesDict({
            'action_log_probs': float64,
            'qpos': Tensor(shape=(6,), dtype=float64),
            'qvel': Tensor(shape=(6,), dtype=float64),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(11,), dtype=float64),
        'reward': float64,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
algorithm Tensor object
iteration Tensor int32
steps Dataset
steps/action Tensor (3,) float64
steps/discount Tensor float64
steps/infos FeaturesDict
steps/infos/action_log_probs Tensor float64
steps/infos/qpos Tensor (6,) float64
steps/infos/qvel Tensor (6,) float64
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (11,) float64
steps/reward Tensor float64

d4rl_mujoco_hopper/v1-full-replay

  • Download size: 183.32 MiB

  • Dataset size: 114.78 MiB

  • Auto-cached (documentation): Yes

  • Splits:

Split Examples
'train' 2,907
  • Feature structure:
FeaturesDict({
    'algorithm': object,
    'iteration': int32,
    'steps': Dataset({
        'action': Tensor(shape=(3,), dtype=float64),
        'discount': float64,
        'infos': FeaturesDict({
            'action_log_probs': float64,
            'qpos': Tensor(shape=(6,), dtype=float64),
            'qvel': Tensor(shape=(6,), dtype=float64),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(11,), dtype=float64),
        'reward': float64,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
algorithm Tensor object
iteration Tensor int32
steps Dataset
steps/action Tensor (3,) float64
steps/discount Tensor float64
steps/infos FeaturesDict
steps/infos/action_log_probs Tensor float64
steps/infos/qpos Tensor (6,) float64
steps/infos/qvel Tensor (6,) float64
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (11,) float64
steps/reward Tensor float64

d4rl_mujoco_hopper/v1-random

  • Download size: 91.11 MiB

  • Dataset size: 130.73 MiB

  • Auto-cached (documentation): Only when shuffle_files=False (train)

  • Splits:

Split Examples
'train' 45,265
  • Feature structure:
FeaturesDict({
    'steps': Dataset({
        'action': Tensor(shape=(3,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'action_log_probs': float32,
            'qpos': Tensor(shape=(6,), dtype=float32),
            'qvel': Tensor(shape=(6,), dtype=float32),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(11,), dtype=float32),
        'reward': float32,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
steps Dataset
steps/action Tensor (3,) float32
steps/discount Tensor float32
steps/infos FeaturesDict
steps/infos/action_log_probs Tensor float32
steps/infos/qpos Tensor (6,) float32
steps/infos/qvel Tensor (6,) float32
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (11,) float32
steps/reward Tensor float32

d4rl_mujoco_hopper/v2-expert

  • Download size: 145.37 MiB

  • Dataset size: 390.40 MiB

  • Auto-cached (documentation): No

  • Splits:

Split Examples
'train' 1,028
  • Feature structure:
FeaturesDict({
    'algorithm': object,
    'iteration': int32,
    'policy': FeaturesDict({
        'fc0': FeaturesDict({
            'bias': Tensor(shape=(256,), dtype=float32),
            'weight': Tensor(shape=(256, 11), dtype=float32),
        }),
        'fc1': FeaturesDict({
            'bias': Tensor(shape=(256,), dtype=float32),
            'weight': Tensor(shape=(256, 256), dtype=float32),
        }),
        'last_fc': FeaturesDict({
            'bias': Tensor(shape=(3,), dtype=float32),
            'weight': Tensor(shape=(3, 256), dtype=float32),
        }),
        'last_fc_log_std': FeaturesDict({
            'bias': Tensor(shape=(3,), dtype=float32),
            'weight': Tensor(shape=(3, 256), dtype=float32),
        }),
        'nonlinearity': object,
        'output_distribution': object,
    }),
    'steps': Dataset({
        'action': Tensor(shape=(3,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'action_log_probs': float64,
            'qpos': Tensor(shape=(6,), dtype=float64),
            'qvel': Tensor(shape=(6,), dtype=float64),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(11,), dtype=float32),
        'reward': float32,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
algorithm Tensor object
iteration Tensor int32
policy FeaturesDict
policy/fc0 FeaturesDict
policy/fc0/bias Tensor (256,) float32
policy/fc0/weight Tensor (256, 11) 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 (3,) float32
policy/last_fc/weight Tensor (3, 256) float32
policy/last_fc_log_std FeaturesDict
policy/last_fc_log_std/bias Tensor (3,) float32
policy/last_fc_log_std/weight Tensor (3, 256) float32
policy/nonlinearity Tensor object
policy/output_distribution Tensor object
steps Dataset
steps/action Tensor (3,) float32
steps/discount Tensor float32
steps/infos FeaturesDict
steps/infos/action_log_probs Tensor float64
steps/infos/qpos Tensor (6,) float64
steps/infos/qvel Tensor (6,) float64
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (11,) float32
steps/reward Tensor float32

d4rl_mujoco_hopper/v2-full-replay

  • Download size: 179.29 MiB

  • Dataset size: 115.04 MiB

  • Auto-cached (documentation): Yes

  • Splits:

Split Examples
'train' 3,515
  • Feature structure:
FeaturesDict({
    'algorithm': object,
    'iteration': int32,
    'steps': Dataset({
        'action': Tensor(shape=(3,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'action_log_probs': float64,
            'qpos': Tensor(shape=(6,), dtype=float64),
            'qvel': Tensor(shape=(6,), dtype=float64),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(11,), dtype=float32),
        'reward': float32,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
algorithm Tensor object
iteration Tensor int32
steps Dataset
steps/action Tensor (3,) float32
steps/discount Tensor float32
steps/infos FeaturesDict
steps/infos/action_log_probs Tensor float64
steps/infos/qpos Tensor (6,) float64
steps/infos/qvel Tensor (6,) float64
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (11,) float32
steps/reward Tensor float32

d4rl_mujoco_hopper/v2-medium

  • Download size: 145.68 MiB

  • Dataset size: 702.57 MiB

  • Auto-cached (documentation): No

  • Splits:

Split Examples
'train' 2,187
  • Feature structure:
FeaturesDict({
    'algorithm': object,
    'iteration': int32,
    'policy': FeaturesDict({
        'fc0': FeaturesDict({
            'bias': Tensor(shape=(256,), dtype=float32),
            'weight': Tensor(shape=(256, 11), dtype=float32),
        }),
        'fc1': FeaturesDict({
            'bias': Tensor(shape=(256,), dtype=float32),
            'weight': Tensor(shape=(256, 256), dtype=float32),
        }),
        'last_fc': FeaturesDict({
            'bias': Tensor(shape=(3,), dtype=float32),
            'weight': Tensor(shape=(3, 256), dtype=float32),
        }),
        'last_fc_log_std': FeaturesDict({
            'bias': Tensor(shape=(3,), dtype=float32),
            'weight': Tensor(shape=(3, 256), dtype=float32),
        }),
        'nonlinearity': object,
        'output_distribution': object,
    }),
    'steps': Dataset({
        'action': Tensor(shape=(3,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'action_log_probs': float64,
            'qpos': Tensor(shape=(6,), dtype=float64),
            'qvel': Tensor(shape=(6,), dtype=float64),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(11,), dtype=float32),
        'reward': float32,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
algorithm Tensor object
iteration Tensor int32
policy FeaturesDict
policy/fc0 FeaturesDict
policy/fc0/bias Tensor (256,) float32
policy/fc0/weight Tensor (256, 11) 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 (3,) float32
policy/last_fc/weight Tensor (3, 256) float32
policy/last_fc_log_std FeaturesDict
policy/last_fc_log_std/bias Tensor (3,) float32
policy/last_fc_log_std/weight Tensor (3, 256) float32
policy/nonlinearity Tensor object
policy/output_distribution Tensor object
steps Dataset
steps/action Tensor (3,) float32
steps/discount Tensor float32
steps/infos FeaturesDict
steps/infos/action_log_probs Tensor float64
steps/infos/qpos Tensor (6,) float64
steps/infos/qvel Tensor (6,) float64
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (11,) float32
steps/reward Tensor float32

d4rl_mujoco_hopper/v2-medium-expert

  • Download size: 290.43 MiB

  • Dataset size: 228.28 MiB

  • Auto-cached (documentation): Only when shuffle_files=False (train)

  • Splits:

Split Examples
'train' 3,214
  • Feature structure:
FeaturesDict({
    'steps': Dataset({
        'action': Tensor(shape=(3,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'action_log_probs': float64,
            'qpos': Tensor(shape=(6,), dtype=float64),
            'qvel': Tensor(shape=(6,), dtype=float64),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(11,), dtype=float32),
        'reward': float32,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
steps Dataset
steps/action Tensor (3,) float32
steps/discount Tensor float32
steps/infos FeaturesDict
steps/infos/action_log_probs Tensor float64
steps/infos/qpos Tensor (6,) float64
steps/infos/qvel Tensor (6,) float64
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (11,) float32
steps/reward Tensor float32

d4rl_mujoco_hopper/v2-medium-replay

  • Download size: 72.34 MiB

  • Dataset size: 46.51 MiB

  • Auto-cached (documentation): Yes

  • Splits:

Split Examples
'train' 2,041
  • Feature structure:
FeaturesDict({
    'algorithm': object,
    'iteration': int32,
    'steps': Dataset({
        'action': Tensor(shape=(3,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'action_log_probs': float64,
            'qpos': Tensor(shape=(6,), dtype=float64),
            'qvel': Tensor(shape=(6,), dtype=float64),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(11,), dtype=float32),
        'reward': float32,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
algorithm Tensor object
iteration Tensor int32
steps Dataset
steps/action Tensor (3,) float32
steps/discount Tensor float32
steps/infos FeaturesDict
steps/infos/action_log_probs Tensor float64
steps/infos/qpos Tensor (6,) float64
steps/infos/qvel Tensor (6,) float64
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (11,) float32
steps/reward Tensor float32

d4rl_mujoco_hopper/v2-random

  • Download size: 145.46 MiB

  • Dataset size: 130.72 MiB

  • Auto-cached (documentation): Only when shuffle_files=False (train)

  • Splits:

Split Examples
'train' 45,240
  • Feature structure:
FeaturesDict({
    'steps': Dataset({
        'action': Tensor(shape=(3,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'action_log_probs': float64,
            'qpos': Tensor(shape=(6,), dtype=float64),
            'qvel': Tensor(shape=(6,), dtype=float64),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(11,), dtype=float32),
        'reward': float32,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
steps Dataset
steps/action Tensor (3,) float32
steps/discount Tensor float32
steps/infos FeaturesDict
steps/infos/action_log_probs Tensor float64
steps/infos/qpos Tensor (6,) float64
steps/infos/qvel Tensor (6,) float64
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (11,) float32
steps/reward Tensor float32