Fishwood#

Action Space

Discrete(2)

Observation Space

Discrete(2)

Reward Shape

(2,)

Reward High

[1. 1.]

Reward Low

[0. 0.]

Import

mo_gymnasium.make("fishwood-v0")

Description#

The FishWood environment is a simple MORL problem in which the agent controls a fisherman which can either fish or go collect wood. From Multi-objective Reinforcement Learning for the Expected Utility of the Return.

Observation Space#

The observation space is a discrete space with two states:

  • 0: fishing

  • 1: in the woods

Action Space#

The actions is a discrete space where:

  • 0: go fishing

  • 1: go collect wood

Reward Space#

The reward is 2-dimensional:

  • 0: +1 if agent is in the woods, with woodproba probability, and 0 otherwise

  • 1: +1 if the agent is fishing, with fishproba probability, and 0 otherwise

Starting State#

Agent starts in the woods

Termination#

The episode ends after MAX_TS=200 steps

Arguments#

  • fishproba: probability of catching a fish when fishing

  • woodproba: probability of collecting wood when in the woods

Credits#

Code provided by Denis Steckelmacher