Umaze#

Description#

The data is collected from the PointMaze_UMaze-v3 environment, which contains a U shape maze. The agent uses a PD controller to follow a path of waypoints generated with QIteration until it reaches the goal. The task is continuing which means that when the agent reaches the goal the environment generates a new random goal without resetting the location of the agent. The reward function is sparse, only returning a value of 1 if the goal is reached, otherwise 0. To add variance to the collected paths random noise is added to the actions taken by the agent.

Dataset Specs#

Total Timesteps

1000000

Total Episodes

13289

Dataset Observation Space

Dict('achieved_goal': Box(-inf, inf, (2,), float64), 'desired_goal': Box(-inf, inf, (2,), float64), 'observation': Box(-inf, inf, (4,), float64))

Dataset Action Space

Box(-1.0, 1.0, (2,), float32)

Algorithm

QIteration

Author

Rodrigo Perez-Vicente

Email

rperezvicente@farama.org

Code Permalink

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

Minari Version

~=0.4

Download

minari.download_dataset("pointmaze-umaze-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('pointmaze-umaze-v1')
env  = dataset.recover_environment()

ID

PointMaze_UMaze-v3

Observation Space

Dict('achieved_goal': Box(-inf, inf, (2,), float64), 'desired_goal': Box(-inf, inf, (2,), float64), 'observation': Box(-inf, inf, (4,), float64))

Action Space

Box(-1.0, 1.0, (2,), float32)

entry_point

gymnasium_robotics.envs.maze.point_maze:PointMazeEnv

max_episode_steps

1000000.0

reward_threshold

None

nondeterministic

False

order_enforce

True

autoreset

False

disable_env_checker

False

kwargs

{'maze_map': [[1, 1, 1, 1, 1], [1, 0, 0, 0, 1], [1, 1, 1, 0, 1], [1, 0, 0, 0, 1], [1, 1, 1, 1, 1]], 'reward_type': 'sparse', 'continuing_task': True, 'reset_target': True}

additional_wrappers

()

vector_entry_point

None

Evaluation Environment Specs#

Note

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

import minari

dataset = minari.load_dataset('pointmaze-umaze-v1')
eval_env  = dataset.recover_environment(eval_env=True)

ID

PointMaze_UMaze-v3

Observation Space

Dict('achieved_goal': Box(-inf, inf, (2,), float64), 'desired_goal': Box(-inf, inf, (2,), float64), 'observation': Box(-inf, inf, (4,), float64))

Action Space

Box(-1.0, 1.0, (2,), float32)

entry_point

gymnasium_robotics.envs.maze.point_maze:PointMazeEnv

max_episode_steps

300

reward_threshold

None

nondeterministic

False

order_enforce

True

autoreset

False

disable_env_checker

False

kwargs

{'maze_map': [[1, 1, 1, 1, 1], [1, 'g', 0, 0, 1], [1, 1, 1, 0, 1], [1, 0, 0, 0, 1], [1, 1, 1, 1, 1]], 'reward_type': 'sparse', 'continuing_task': True, 'reset_target': False}

additional_wrappers

()

vector_entry_point

None