Minecart-Deterministic

../../_images/minecart-deterministic.gif

Action Space

Discrete(6)

Observation Shape

(7,)

Observation High

[1. 1. 1. 1. 1. 1. 1.]

Observation Low

[-1. -1. -1. -1. -1. -1. -1.]

Reward Shape

(3,)

Reward High

[1.5 1.5 0. ]

Reward Low

[ 0. 0. -1.]

Import

mo_gymnasium.make("minecart-deterministic-v0")

Description

Agent must collect two types of ores and minimize fuel consumption. From Abels et al. 2019.

Observation Space

The observation is a 7-dimensional vector containing the following information:

  • 2D position of the cart

  • Speed of the cart

  • sin and cos of the cart’s orientation

  • porcentage of the capacity of the cart filled If image_observation is True, the observation is a 3D image of the environment.

Action Space

The action space is a discrete space with 6 actions:

  • 0: Mine

  • 1: Left

  • 2: Right

  • 3: Accelerate

  • 4: Brake

  • 5: None

Reward Space

The reward is a 3D vector:

  • 0: Quantity of the first minerium that was retrieved to the base (sparse)

  • 1: Quantity of the second minerium that was retrieved to the base (sparse)

  • 2: Fuel consumed (dense)

Starting State

The cart starts at the base on the upper left corner of the map.

Episode Termination

The episode ends when the cart returns to the base.

Arguments

  • render_mode: The render mode to use. Can be “rgb_array” or “human”.

  • image_observation: If True, the observation is a RGB image of the environment.

  • frame_skip: How many times each action is repeated. Default: 4

  • incremental_frame_skip: Whether actions are repeated incrementally. Default: True

  • config: Path to the .json configuration file. See the default configuration file for more information: https://github.com/Farama-Foundation/MO-Gymnasium/blob/main/mo_gymnasium/envs/minecart/mine_config.json

Credits

The code was refactored from Axel Abels’ source.