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.

• Additional Documentation: Explore on Papers With Code

• Config description: See more details about the task and its versions in https://github.com/rail-berkeley/d4rl/wiki/Tasks#gym

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

• Source code: tfds.d4rl.d4rl_mujoco_walker2d.D4rlMujocoWalker2d

• Versions:

  • 1.0.0: Initial release.
  • 1.1.0: Added is_last.
  • 1.2.0 (default): Updated to take into account the next observation.

• Supervised keys (See as_supervised doc): None

• Figure (tfds.show_examples): Not supported.

• Citation:

@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

• Examples (tfds.as_dataframe):

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

• Examples (tfds.as_dataframe):

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

• Examples (tfds.as_dataframe):

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

• Examples (tfds.as_dataframe):

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

• Examples (tfds.as_dataframe):

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': 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,
    }),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
algorithm	Tensor		string
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		string
policy/output_distribution	Tensor		string
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

• Examples (tfds.as_dataframe):

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': 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,
    }),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
algorithm	Tensor		string
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		string
policy/output_distribution	Tensor		string
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

• Examples (tfds.as_dataframe):

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

• Examples (tfds.as_dataframe):

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': 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,
    }),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
algorithm	Tensor		string
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

• Examples (tfds.as_dataframe):

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': 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,
    }),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
algorithm	Tensor		string
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

• Examples (tfds.as_dataframe):

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

• Examples (tfds.as_dataframe):

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': 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,
    }),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
algorithm	Tensor		string
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		string
policy/output_distribution	Tensor		string
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

• Examples (tfds.as_dataframe):

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': 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,
    }),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
algorithm	Tensor		string
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

• Examples (tfds.as_dataframe):

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': 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,
    }),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
algorithm	Tensor		string
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		string
policy/output_distribution	Tensor		string
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

• Examples (tfds.as_dataframe):

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

• Examples (tfds.as_dataframe):

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': 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,
    }),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
algorithm	Tensor		string
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

• Examples (tfds.as_dataframe):

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

• Examples (tfds.as_dataframe):