locomotion

• Description:

The datasets were created with a SAC agent trained on the environment reward of MuJoCo locomotion tasks. These datasets are used in What Matters for Adversarial Imitation Learning? Orsini et al. 2021.

The datasets follow the RLDS format to represent steps and episodes.s

• Homepage: https://github.com/google-research/rlds

• Source code: tfds.rlds.datasets.locomotion.Locomotion

• Versions:

  • 1.0.0 (default): Initial release.

• Supervised keys (See as_supervised doc): None

• Figure (tfds.show_examples): Not supported.

• Citation:

@article{orsini2021matters,
  title={What Matters for Adversarial Imitation Learning?},
  author={Orsini, Manu and Raichuk, Anton and Hussenot, L{'e}onard and Vincent, Damien and Dadashi, Robert and Girgin, Sertan and Geist, Matthieu and Bachem, Olivier and Pietquin, Olivier and Andrychowicz, Marcin},
  journal={International Conference in Machine Learning},
  year={2021}
}

locomotion/ant_sac_1M_single_policy_stochastic (default config)

• Config description: Dataset generated by a SAC agent trained for 1M steps for Ant.

• Download size: 6.49 MiB

• Dataset size: 23.02 MiB

• Auto-cached (documentation): Yes

• Splits:

Split	Examples
'train'	50

• Feature structure:

FeaturesDict({
    'steps': Dataset({
        'action': Tensor(shape=(8,), dtype=float32),
        'discount': float32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(111,), dtype=float32),
        'reward': float32,
    }),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
steps	Dataset
steps/action	Tensor	(8,)	float32
steps/discount	Tensor		float32
steps/is_first	Tensor		bool
steps/is_last	Tensor		bool
steps/is_terminal	Tensor		bool
steps/observation	Tensor	(111,)	float32
steps/reward	Tensor		float32

• Examples (tfds.as_dataframe):

locomotion/hopper_sac_1M_single_policy_stochastic

• Config description: Dataset generated by a SAC agent trained for 1M steps for Hopper.

• Download size: 2.26 MiB

• Dataset size: 2.62 MiB

• Auto-cached (documentation): Yes

• Splits:

Split	Examples
'train'	50

• 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

• Examples (tfds.as_dataframe):

locomotion/halfcheetah_sac_1M_single_policy_stochastic

• Config description: Dataset generated by a SAC agent trained for 1M steps for HalfCheetah.

• Download size: 4.49 MiB

• Dataset size: 4.93 MiB

• Auto-cached (documentation): Yes

• Splits:

Split	Examples
'train'	50

• 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):

locomotion/walker2d_sac_1M_single_policy_stochastic

• Config description: Dataset generated by a SAC agent trained for 1M steps for Walker2d.

• Download size: 4.35 MiB

• Dataset size: 4.91 MiB

• Auto-cached (documentation): Yes

• Splits:

Split	Examples
'train'	50

• 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):

locomotion/humanoid_sac_15M_single_policy_stochastic

• Config description: Dataset generated by a SAC agent trained for 15M steps for Humanoid.

• Download size: 192.78 MiB

• Dataset size: 300.94 MiB

• Auto-cached (documentation): No

• Splits:

Split	Examples
'train'	200

• Feature structure:

FeaturesDict({
    'steps': Dataset({
        'action': Tensor(shape=(17,), dtype=float32),
        'discount': float32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': Tensor(shape=(376,), dtype=float32),
        'reward': float32,
    }),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
steps	Dataset
steps/action	Tensor	(17,)	float32
steps/discount	Tensor		float32
steps/is_first	Tensor		bool
steps/is_last	Tensor		bool
steps/is_terminal	Tensor		bool
steps/observation	Tensor	(376,)	float32
steps/reward	Tensor		float32

• Examples (tfds.as_dataframe):