Fruit-Tree#


Action Space	Discrete(2)
Observation Shape	(2,)
Observation High	[63 63]
Observation Low	[0 0]
Reward Shape	(6,)
Reward High	[10. 10. 10. 10. 10. 10.]
Reward Low	[0. 0. 0. 0. 0. 0.]
Import	`mo_gymnasium.make("fruit-tree-v0")`

Description#

Full binary tree of depth d=5,6 or 7. Every leaf contains a fruit with a value for the nutrients Protein, Carbs, Fats, Vitamins, Minerals and Water. From Yang et al. 2019.

Observation Space#

Discrete space of size 2^d-1, where d is the depth of the tree.

Action Space#

The agent can chose to go left or right at every node. The action space is therefore a discrete space of size 2.

Reward Space#

Each leaf node contains a 6-dimensional vector containing the nutrients of the fruit. The agent receives a reward for each nutrient it collects.

Starting State#

The agent starts at the root node (0, 0).

Episode Termination#

The episode terminates when the agent reaches a leaf node.