Fourrooms¶

Description¶

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

Dataset Specs¶


Total Steps	10010
Total Episodes	590
Algorithm	ExpertPolicy
Author	Omar G. Younis
Email	omar.younis98@gmail.com
Code Permalink	https://github.com/rodrigodelazcano/d4rl-minari-dataset-generation
Minari Version	`0.4.3` (supported)
Download	`minari download D4RL/minigrid/fourrooms-v0`

Environment Specs¶

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('D4RL/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 0x7f3c608d44c0>, None))`
Action Space	`Discrete(7)`
entry_point	`minigrid.envs:FourRoomsEnv`
max_episode_steps	None
reward_threshold	None
nondeterministic	`False`
order_enforce	`True`
disable_env_checker	`False`
kwargs	`{}`
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/minigrid/fourrooms-v0')
env  = dataset.recover_environment()
eval_env = dataset.recover_environment(eval_env=True)

assert env.spec == eval_env.spec