Cloned

Description

Data obtained by training an imitation policy on the demonstrations from expert and human, then running the policy, and mixing data at a 50-50 ratio with the demonstrations. This dataset is provided by D4RL. The environment used to collect the dataset is AdroitHandDoor-v1.

Dataset Specs

Total Steps	1000000
Total Episodes	4358
Dataset Observation Space	Box(-inf, inf, (39,), float64)
Dataset Action Space	Box(-1.0, 1.0, (28,), float32)
Algorithm	Not provided
Author	Rodrigo de Lazcano
Email	rperezvicente@farama.org
Code Permalink	https://github.com/rodrigodelazcano/d4rl-minari-dataset-generation
Minari Version	0.4.3 (supported)
Download	minari download D4RL/door/cloned-v2

Environment Specs

The following table rows correspond to 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('D4RL/door/cloned-v2')
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
disable_env_checker	False
kwargs	{'reward_type': 'dense'}
additional_wrappers	()
vector_entry_point	None

Evaluation Environment Specs

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('D4RL/door/cloned-v2')
env  = dataset.recover_environment()
eval_env = dataset.recover_environment(eval_env=True)

assert env.spec == eval_env.spec