Fourrooms

Description

This dataset was generated using an expert policy with full observability that goes straight to the goal.

Dataset Specs

Total Timesteps	10010
Total Episodes	590
Dataset Observation Space	Dict('direction': Discrete(4), 'image': Box(0, 255, (7, 7, 3), uint8), 'mission': Text(1, 14, charset=                                                              ''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''(),,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdeeeffghijklmnnoopqrrssttuvwxyzz{}))
Dataset Action Space	Discrete(7)
Algorithm	ExpertPolicy
Author	Omar G. Younis
Email	omar.younis98@gmail.com
Code Permalink	https://github.com/rodrigodelazcano/d4rl-minari-dataset-generation
Minari Version	>=0.4.0
Download	minari.download_dataset("minigrid-fourrooms-v0")

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('minigrid-fourrooms-v0')
env  = dataset.recover_environment()

ID	MiniGrid-FourRooms-v0
Observation Space	Dict('direction': Discrete(4), 'image': Box(0, 255, (7, 7, 3), uint8), 'mission': MissionSpace(<function FourRoomsEnv._gen_mission at 0x7fd58c3ccc10>, None))
Action Space	Discrete(7)
entry_point	minigrid.envs:FourRoomsEnv
max_episode_steps	None
reward_threshold	None
nondeterministic	False
order_enforce	True
autoreset	False
disable_env_checker	False
kwargs	{}
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('minigrid-fourrooms-v0')
env  = dataset.recover_environment()
eval_env = dataset.recover_environment(eval_env=True)

assert env.spec == eval_env.spec