Attend the Women in ML Symposium on December 7 Register now

d4rl_mujoco_walker2d

  • 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_walker2d/v0-expert (default config)

  • Download size: 78.41 MiB

  • Dataset size: 98.64 MiB

  • Auto-cached (documentation): Yes

  • Splits:

Split Examples
'train' 1,628
  • Feature structure:
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,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
steps Dataset
steps/action Tensor (6,) float32
steps/discount Tensor float32
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (17,) float32
steps/reward Tensor float32

d4rl_mujoco_walker2d/v0-medium

  • Download size: 80.83 MiB

  • Dataset size: 99.72 MiB

  • Auto-cached (documentation): Yes

  • Splits:

Split Examples
'train' 5,315
  • Feature structure:
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,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
steps Dataset
steps/action Tensor (6,) float32
steps/discount Tensor float32
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (17,) float32
steps/reward Tensor float32

d4rl_mujoco_walker2d/v0-medium-expert

  • Download size: 159.24 MiB

  • Dataset size: 198.36 MiB

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

  • Splits:

Split Examples
'train' 6,943
  • Feature structure:
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,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
steps Dataset
steps/action Tensor (6,) float32
steps/discount Tensor float32
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (17,) float32
steps/reward Tensor float32

d4rl_mujoco_walker2d/v0-mixed

  • Download size: 8.42 MiB

  • Dataset size: 10.06 MiB

  • Auto-cached (documentation): Yes

  • Splits:

Split Examples
'train' 501
  • Feature structure:
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,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
steps Dataset
steps/action Tensor (6,) float32
steps/discount Tensor float32
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (17,) float32
steps/reward Tensor float32

d4rl_mujoco_walker2d/v0-random

  • Download size: 78.41 MiB

  • Dataset size: 112.04 MiB

  • Auto-cached (documentation): Yes

  • Splits:

Split Examples
'train' 50,988
  • Feature structure:
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,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
steps Dataset
steps/action Tensor (6,) float32
steps/discount Tensor float32
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (17,) float32
steps/reward Tensor float32

d4rl_mujoco_walker2d/v1-expert

  • Download size: 143.06 MiB

  • Dataset size: 452.72 MiB

  • Auto-cached (documentation): No

  • Splits:

Split Examples
'train' 1,003
  • Feature structure:
FeaturesDict({
    'algorithm': object,
    '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': object,
        'output_distribution': object,
    }),
    '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,
    }),
})
  • 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, 17) 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 (6,) float32
policy/last_fc/weight Tensor (6, 256) float32
policy/last_fc_log_std FeaturesDict
policy/last_fc_log_std/bias Tensor (6,) float32
policy/last_fc_log_std/weight Tensor (6, 256) float32
policy/nonlinearity Tensor object
policy/output_distribution Tensor object
steps Dataset
steps/action Tensor (6,) float32
steps/discount Tensor float32
steps/infos FeaturesDict
steps/infos/action_log_probs Tensor float32
steps/infos/qpos Tensor (9,) float32
steps/infos/qvel Tensor (9,) float32
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (17,) float32
steps/reward Tensor float32

d4rl_mujoco_walker2d/v1-medium

  • Download size: 144.23 MiB

  • Dataset size: 510.08 MiB

  • Auto-cached (documentation): No

  • Splits:

Split Examples
'train' 1,207
  • Feature structure:
FeaturesDict({
    'algorithm': object,
    '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': object,
        'output_distribution': object,
    }),
    '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,
    }),
})
  • 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, 17) 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 (6,) float32
policy/last_fc/weight Tensor (6, 256) float32
policy/last_fc_log_std FeaturesDict
policy/last_fc_log_std/bias Tensor (6,) float32
policy/last_fc_log_std/weight Tensor (6, 256) float32
policy/nonlinearity Tensor object
policy/output_distribution Tensor object
steps Dataset
steps/action Tensor (6,) float32
steps/discount Tensor float32
steps/infos FeaturesDict
steps/infos/action_log_probs Tensor float32
steps/infos/qpos Tensor (9,) float32
steps/infos/qvel Tensor (9,) float32
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (17,) float32
steps/reward Tensor float32

d4rl_mujoco_walker2d/v1-medium-expert

  • Download size: 286.69 MiB

  • Dataset size: 342.46 MiB

  • Auto-cached (documentation): No

  • Splits:

Split Examples
'train' 2,209
  • Feature structure:
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,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
steps Dataset
steps/action Tensor (6,) float32
steps/discount Tensor float32
steps/infos FeaturesDict
steps/infos/action_log_probs Tensor float32
steps/infos/qpos Tensor (9,) float32
steps/infos/qvel Tensor (9,) float32
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (17,) float32
steps/reward Tensor float32

d4rl_mujoco_walker2d/v1-medium-replay

  • Download size: 84.37 MiB

  • Dataset size: 52.10 MiB

  • Auto-cached (documentation): Yes

  • Splits:

Split Examples
'train' 1,093
  • Feature structure:
FeaturesDict({
    'algorithm': object,
    '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,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
algorithm Tensor object
iteration Tensor int32
steps Dataset
steps/action Tensor (6,) float64
steps/discount Tensor float64
steps/infos FeaturesDict
steps/infos/action_log_probs Tensor float64
steps/infos/qpos Tensor (9,) float64
steps/infos/qvel Tensor (9,) float64
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (17,) float64
steps/reward Tensor float64

d4rl_mujoco_walker2d/v1-full-replay

  • Download size: 278.95 MiB

  • Dataset size: 171.66 MiB

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

  • Splits:

Split Examples
'train' 1,888
  • Feature structure:
FeaturesDict({
    'algorithm': object,
    '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,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
algorithm Tensor object
iteration Tensor int32
steps Dataset
steps/action Tensor (6,) float64
steps/discount Tensor float64
steps/infos FeaturesDict
steps/infos/action_log_probs Tensor float64
steps/infos/qpos Tensor (9,) float64
steps/infos/qvel Tensor (9,) float64
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (17,) float64
steps/reward Tensor float64

d4rl_mujoco_walker2d/v1-random

  • Download size: 132.36 MiB

  • Dataset size: 192.06 MiB

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

  • Splits:

Split Examples
'train' 48,790
  • Feature structure:
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,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
steps Dataset
steps/action Tensor (6,) float32
steps/discount Tensor float32
steps/infos FeaturesDict
steps/infos/action_log_probs Tensor float32
steps/infos/qpos Tensor (9,) float32
steps/infos/qvel Tensor (9,) float32
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (17,) float32
steps/reward Tensor float32

d4rl_mujoco_walker2d/v2-expert

  • Download size: 219.89 MiB

  • Dataset size: 452.16 MiB

  • Auto-cached (documentation): No

  • Splits:

Split Examples
'train' 1,001
  • Feature structure:
FeaturesDict({
    'algorithm': object,
    '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': object,
        'output_distribution': object,
    }),
    '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,
    }),
})
  • 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, 17) 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 (6,) float32
policy/last_fc/weight Tensor (6, 256) float32
policy/last_fc_log_std FeaturesDict
policy/last_fc_log_std/bias Tensor (6,) float32
policy/last_fc_log_std/weight Tensor (6, 256) float32
policy/nonlinearity Tensor object
policy/output_distribution Tensor object
steps Dataset
steps/action Tensor (6,) float32
steps/discount Tensor float32
steps/infos FeaturesDict
steps/infos/action_log_probs Tensor float64
steps/infos/qpos Tensor (9,) float64
steps/infos/qvel Tensor (9,) float64
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (17,) float32
steps/reward Tensor float32

d4rl_mujoco_walker2d/v2-full-replay

  • Download size: 271.91 MiB

  • Dataset size: 171.66 MiB

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

  • Splits:

Split Examples
'train' 1,888
  • Feature structure:
FeaturesDict({
    'algorithm': object,
    '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,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
algorithm Tensor object
iteration Tensor int32
steps Dataset
steps/action Tensor (6,) float32
steps/discount Tensor float32
steps/infos FeaturesDict
steps/infos/action_log_probs Tensor float64
steps/infos/qpos Tensor (9,) float64
steps/infos/qvel Tensor (9,) float64
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (17,) float32
steps/reward Tensor float32

d4rl_mujoco_walker2d/v2-medium

  • Download size: 221.50 MiB

  • Dataset size: 505.58 MiB

  • Auto-cached (documentation): No

  • Splits:

Split Examples
'train' 1,191
  • Feature structure:
FeaturesDict({
    'algorithm': object,
    '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': object,
        'output_distribution': object,
    }),
    '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,
    }),
})
  • 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, 17) 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 (6,) float32
policy/last_fc/weight Tensor (6, 256) float32
policy/last_fc_log_std FeaturesDict
policy/last_fc_log_std/bias Tensor (6,) float32
policy/last_fc_log_std/weight Tensor (6, 256) float32
policy/nonlinearity Tensor object
policy/output_distribution Tensor object
steps Dataset
steps/action Tensor (6,) float32
steps/discount Tensor float32
steps/infos FeaturesDict
steps/infos/action_log_probs Tensor float64
steps/infos/qpos Tensor (9,) float64
steps/infos/qvel Tensor (9,) float64
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (17,) float32
steps/reward Tensor float32

d4rl_mujoco_walker2d/v2-medium-expert

  • Download size: 440.79 MiB

  • Dataset size: 342.45 MiB

  • Auto-cached (documentation): No

  • Splits:

Split Examples
'train' 2,191
  • Feature structure:
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,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
steps Dataset
steps/action Tensor (6,) float32
steps/discount Tensor float32
steps/infos FeaturesDict
steps/infos/action_log_probs Tensor float64
steps/infos/qpos Tensor (9,) float64
steps/infos/qvel Tensor (9,) float64
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (17,) float32
steps/reward Tensor float32

d4rl_mujoco_walker2d/v2-medium-replay

  • Download size: 82.32 MiB

  • Dataset size: 52.10 MiB

  • Auto-cached (documentation): Yes

  • Splits:

Split Examples
'train' 1,093
  • Feature structure:
FeaturesDict({
    'algorithm': object,
    '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,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
algorithm Tensor object
iteration Tensor int32
steps Dataset
steps/action Tensor (6,) float32
steps/discount Tensor float32
steps/infos FeaturesDict
steps/infos/action_log_probs Tensor float64
steps/infos/qpos Tensor (9,) float64
steps/infos/qvel Tensor (9,) float64
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (17,) float32
steps/reward Tensor float32

d4rl_mujoco_walker2d/v2-random

  • Download size: 206.10 MiB

  • Dataset size: 192.11 MiB

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

  • Splits:

Split Examples
'train' 48,908
  • Feature structure:
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,
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
steps Dataset
steps/action Tensor (6,) float32
steps/discount Tensor float32
steps/infos FeaturesDict
steps/infos/action_log_probs Tensor float64
steps/infos/qpos Tensor (9,) float64
steps/infos/qvel Tensor (9,) float64
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation Tensor (17,) float32
steps/reward Tensor float32