Human#

Description#

25 human demonstrations provided in the DAPG repository. The environment used to collect the dataset is AdroitHandDoor-v1.

Dataset Specs#

Total Timesteps

6729

Total Episodes

25

Dataset Observation Space

Box(-inf, inf, (39,), float64)

Dataset Action Space

Box(-1.0, 1.0, (28,), float32)

Algorithm

None

Author

Rodrigo de Lazcano

Email

rperezvicente@farama.org

Code Permalink

https://github.com/rodrigodelazcano/d4rl-minari-dataset-generation

Minari Version

~=0.4

download

minari.download_dataset("door-human-v1")

Environment Specs#

Note

The following table rows correspond to (in addition to the action and observation space) the Gymnasium environment specifications used to generate the dataset. To read more about what each parameter means you can have a look at the Gymnasium documentation https://gymnasium.farama.org/api/registry/#gymnasium.envs.registration.EnvSpec

This environment can be recovered from the Minari dataset as follows:

import minari

dataset = minari.load_dataset('door-human-v1')
env  = dataset.recover_environment()

ID

AdroitHandDoor-v1

Observation Space

Box(-inf, inf, (39,), float64)

Action Space

Box(-1.0, 1.0, (28,), float32)

entry_point

gymnasium_robotics.envs.adroit_hand.adroit_door:AdroitHandDoorEnv

max_episode_steps

200

reward_threshold

None

nondeterministic

False

order_enforce

True

autoreset

False

disable_env_checker

False

kwargs

{'reward_type': 'dense'}

additional_wrappers

()

vector_entry_point

None

Evaluation Environment Specs#

Note

This dataset doesn’t contain an eval_env_spec attribute which means that the specs of the environment used for evaluation are the same as the specs of the environment used for creating the dataset. The following calls will return the same environment:

import minari

dataset = minari.load_dataset('door-human-v1')
env  = dataset.recover_environment()
eval_env = dataset.recover_environment(eval_env=True)

assert env.spec == eval_env.spec