TFDS now supports the Croissant 🥐 format! Read the documentation to know more.

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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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):