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d4rl_adroit_door

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.

Config description: See more details about the task and its versions in https://github.com/rail-berkeley/d4rl/wiki/Tasks#adroit
Homepage: https://sites.google.com/view/d4rl-anonymous
Source code: tfds.d4rl.d4rl_adroit_door.D4rlAdroitDoor
Versions:
- 1.0.0: Initial release.
- 1.1.0 (default): Added is_last.
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_adroit_door/v0-human (default config)

Download size: 2.97 MiB
Dataset size: 3.36 MiB
Auto-cached (documentation): Yes
Splits:

Split	Examples
`'train'`	50

Feature structure:

FeaturesDict({
    'steps': Dataset({
        'action': Tensor(shape=(28,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'qpos': Tensor(shape=(30,), dtype=float32),
            'qvel': Tensor(shape=(30,), dtype=float32),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(39,), dtype=float32),
        'reward': float32,
    }),
})

Feature documentation:

Feature	Class	Shape	Dtype
	FeaturesDict
steps	Dataset
steps/action	Tensor	(28,)	float32
steps/discount	Tensor		float32
steps/infos	FeaturesDict
steps/infos/qpos	Tensor	(30,)	float32
steps/infos/qvel	Tensor	(30,)	float32
steps/is_first	Tensor		bool
steps/is_last	Tensor		bool
steps/is_terminal	Tensor		bool
steps/observation	Tensor	(39,)	float32
steps/reward	Tensor		float32

Examples (tfds.as_dataframe):

d4rl_adroit_door/v0-cloned

Download size: 602.42 MiB
Dataset size: 497.47 MiB
Auto-cached (documentation): No
Splits:

Split	Examples
`'train'`	6,214

Feature structure:

FeaturesDict({
    'steps': Dataset({
        'action': Tensor(shape=(28,), dtype=float32),
        'discount': float64,
        'infos': FeaturesDict({
            'qpos': Tensor(shape=(30,), dtype=float64),
            'qvel': Tensor(shape=(30,), dtype=float64),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(39,), dtype=float64),
        'reward': float64,
    }),
})

Feature documentation:

Feature	Class	Shape	Dtype
	FeaturesDict
steps	Dataset
steps/action	Tensor	(28,)	float32
steps/discount	Tensor		float64
steps/infos	FeaturesDict
steps/infos/qpos	Tensor	(30,)	float64
steps/infos/qvel	Tensor	(30,)	float64
steps/is_first	Tensor		bool
steps/is_last	Tensor		bool
steps/is_terminal	Tensor		bool
steps/observation	Tensor	(39,)	float64
steps/reward	Tensor		float64

Examples (tfds.as_dataframe):

d4rl_adroit_door/v0-expert

Download size: 511.05 MiB
Dataset size: 710.30 MiB
Auto-cached (documentation): No
Splits:

Split	Examples
`'train'`	5,000

Feature structure:

FeaturesDict({
    'steps': Dataset({
        'action': Tensor(shape=(28,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'action_logstd': Tensor(shape=(28,), dtype=float32),
            'action_mean': Tensor(shape=(28,), dtype=float32),
            'qpos': Tensor(shape=(30,), dtype=float32),
            'qvel': Tensor(shape=(30,), dtype=float32),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(39,), dtype=float32),
        'reward': float32,
    }),
})

Feature documentation:

Feature	Class	Shape	Dtype
	FeaturesDict
steps	Dataset
steps/action	Tensor	(28,)	float32
steps/discount	Tensor		float32
steps/infos	FeaturesDict
steps/infos/action_logstd	Tensor	(28,)	float32
steps/infos/action_mean	Tensor	(28,)	float32
steps/infos/qpos	Tensor	(30,)	float32
steps/infos/qvel	Tensor	(30,)	float32
steps/is_first	Tensor		bool
steps/is_last	Tensor		bool
steps/is_terminal	Tensor		bool
steps/observation	Tensor	(39,)	float32
steps/reward	Tensor		float32

Examples (tfds.as_dataframe):

d4rl_adroit_door/v1-human

Download size: 2.98 MiB
Dataset size: 3.42 MiB
Auto-cached (documentation): Yes
Splits:

Split	Examples
`'train'`	25

Feature structure:

FeaturesDict({
    'steps': Dataset({
        'action': Tensor(shape=(28,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'door_body_pos': Tensor(shape=(3,), dtype=float32),
            'qpos': Tensor(shape=(30,), dtype=float32),
            'qvel': Tensor(shape=(30,), dtype=float32),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(39,), dtype=float32),
        'reward': float32,
    }),
})

Feature documentation:

Feature	Class	Shape	Dtype
	FeaturesDict
steps	Dataset
steps/action	Tensor	(28,)	float32
steps/discount	Tensor		float32
steps/infos	FeaturesDict
steps/infos/door_body_pos	Tensor	(3,)	float32
steps/infos/qpos	Tensor	(30,)	float32
steps/infos/qvel	Tensor	(30,)	float32
steps/is_first	Tensor		bool
steps/is_last	Tensor		bool
steps/is_terminal	Tensor		bool
steps/observation	Tensor	(39,)	float32
steps/reward	Tensor		float32

Examples (tfds.as_dataframe):

d4rl_adroit_door/v1-cloned

Download size: 280.72 MiB
Dataset size: 1.85 GiB
Auto-cached (documentation): No
Splits:

Split	Examples
`'train'`	4,358

Feature structure:

FeaturesDict({
    'algorithm': string,
    'policy': FeaturesDict({
        'fc0': FeaturesDict({
            'bias': Tensor(shape=(256,), dtype=float32),
            'weight': Tensor(shape=(39, 256), dtype=float32),
        }),
        'fc1': FeaturesDict({
            'bias': Tensor(shape=(256,), dtype=float32),
            'weight': Tensor(shape=(256, 256), dtype=float32),
        }),
        'last_fc': FeaturesDict({
            'bias': Tensor(shape=(28,), dtype=float32),
            'weight': Tensor(shape=(256, 28), dtype=float32),
        }),
        'nonlinearity': string,
        'output_distribution': string,
    }),
    'steps': Dataset({
        'action': Tensor(shape=(28,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'door_body_pos': Tensor(shape=(3,), dtype=float32),
            'qpos': Tensor(shape=(30,), dtype=float32),
            'qvel': Tensor(shape=(30,), dtype=float32),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(39,), dtype=float32),
        'reward': float32,
    }),
})

Feature documentation:

Feature	Class	Shape	Dtype
	FeaturesDict
algorithm	Tensor		string
policy	FeaturesDict
policy/fc0	FeaturesDict
policy/fc0/bias	Tensor	(256,)	float32
policy/fc0/weight	Tensor	(39, 256)	float32
policy/fc1	FeaturesDict
policy/fc1/bias	Tensor	(256,)	float32
policy/fc1/weight	Tensor	(256, 256)	float32
policy/last_fc	FeaturesDict
policy/last_fc/bias	Tensor	(28,)	float32
policy/last_fc/weight	Tensor	(256, 28)	float32
policy/nonlinearity	Tensor		string
policy/output_distribution	Tensor		string
steps	Dataset
steps/action	Tensor	(28,)	float32
steps/discount	Tensor		float32
steps/infos	FeaturesDict
steps/infos/door_body_pos	Tensor	(3,)	float32
steps/infos/qpos	Tensor	(30,)	float32
steps/infos/qvel	Tensor	(30,)	float32
steps/is_first	Tensor		bool
steps/is_last	Tensor		bool
steps/is_terminal	Tensor		bool
steps/observation	Tensor	(39,)	float32
steps/reward	Tensor		float32

Examples (tfds.as_dataframe):

d4rl_adroit_door/v1-expert

Download size: 511.22 MiB
Dataset size: 803.48 MiB
Auto-cached (documentation): No
Splits:

Split	Examples
`'train'`	5,000

Feature structure:

FeaturesDict({
    'algorithm': string,
    'policy': FeaturesDict({
        'fc0': FeaturesDict({
            'bias': Tensor(shape=(32,), dtype=float32),
            'weight': Tensor(shape=(32, 39), dtype=float32),
        }),
        'fc1': FeaturesDict({
            'bias': Tensor(shape=(32,), dtype=float32),
            'weight': Tensor(shape=(32, 32), dtype=float32),
        }),
        'last_fc': FeaturesDict({
            'bias': Tensor(shape=(28,), dtype=float32),
            'weight': Tensor(shape=(28, 32), dtype=float32),
        }),
        'last_fc_log_std': FeaturesDict({
            'bias': Tensor(shape=(28,), dtype=float32),
            'weight': Tensor(shape=(28, 32), dtype=float32),
        }),
        'nonlinearity': string,
        'output_distribution': string,
    }),
    'steps': Dataset({
        'action': Tensor(shape=(28,), dtype=float32),
        'discount': float32,
        'infos': FeaturesDict({
            'action_log_std': Tensor(shape=(28,), dtype=float32),
            'action_mean': Tensor(shape=(28,), dtype=float32),
            'door_body_pos': Tensor(shape=(3,), dtype=float32),
            'qpos': Tensor(shape=(30,), dtype=float32),
            'qvel': Tensor(shape=(30,), dtype=float32),
        }),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(39,), dtype=float32),
        'reward': float32,
    }),
})

Feature documentation:

Feature	Class	Shape	Dtype
	FeaturesDict
algorithm	Tensor		string
policy	FeaturesDict
policy/fc0	FeaturesDict
policy/fc0/bias	Tensor	(32,)	float32
policy/fc0/weight	Tensor	(32, 39)	float32
policy/fc1	FeaturesDict
policy/fc1/bias	Tensor	(32,)	float32
policy/fc1/weight	Tensor	(32, 32)	float32
policy/last_fc	FeaturesDict
policy/last_fc/bias	Tensor	(28,)	float32
policy/last_fc/weight	Tensor	(28, 32)	float32
policy/last_fc_log_std	FeaturesDict
policy/last_fc_log_std/bias	Tensor	(28,)	float32
policy/last_fc_log_std/weight	Tensor	(28, 32)	float32
policy/nonlinearity	Tensor		string
policy/output_distribution	Tensor		string
steps	Dataset
steps/action	Tensor	(28,)	float32
steps/discount	Tensor		float32
steps/infos	FeaturesDict
steps/infos/action_log_std	Tensor	(28,)	float32
steps/infos/action_mean	Tensor	(28,)	float32
steps/infos/door_body_pos	Tensor	(3,)	float32
steps/infos/qpos	Tensor	(30,)	float32
steps/infos/qvel	Tensor	(30,)	float32
steps/is_first	Tensor		bool
steps/is_last	Tensor		bool
steps/is_terminal	Tensor		bool
steps/observation	Tensor	(39,)	float32
steps/reward	Tensor		float32

Examples (tfds.as_dataframe):