Water-Reservoir#

../../_images/water-reservoir.gif

Action Space

Box(0.0, inf, (1,), float32)

Observation Shape

(1,)

Observation High

[inf]

Observation Low

[0.]

Reward Shape

(2,)

Reward High

[0. 0.]

Reward Low

[-inf -inf]

Import

mo_gymnasium.make("water-reservoir-v0")

Description#

A Water reservoir environment. The agent executes a continuous action, corresponding to the amount of water released by the dam.

A. Castelletti, F. Pianosi and M. Restelli, “Tree-based Fitted Q-iteration for Multi-Objective Markov Decision problems,” The 2012 International Joint Conference on Neural Networks (IJCNN), Brisbane, QLD, Australia, 2012, pp. 1-8, doi: 10.1109/IJCNN.2012.6252759.

Observation Space#

The observation is a float corresponding to the current level of the reservoir.

Action Space#

The action is a float corresponding to the amount of water released by the dam.

Reward Space#

There are up to 4 rewards:

  • cost due to excess level wrt a flooding threshold (upstream)

  • deficit in the water supply wrt the water demand

  • deficit in hydroelectric supply wrt hydroelectric demand

  • cost due to excess level wrt a flooding threshold (downstream)

Credits#

Code from: Mathieu Reymond. Ported from: Simone Parisi.

Sky background image from: Paulina Riva (https://opengameart.org/content/sky-background)