DeepMobLearningReloaded
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Deep Mob Learning: based on Reforged, modified for easier integration with external mods
Does not follow a specific thematic focus apart from vanilla Minecraft.
Focuses on automation and resource processing through scientific and mechanical means, utilizing systems like electricity (RF/FE/etc), air pressure, or rotation.
Features machines that process raw materials or automation processes.
Adds extra equipment such special items and accessories specifically using the Baubles or Curios mods.
Adds or enhances hostile creatures and enemies.
Adds extra features or content to an existing mod.
Forge is a popular mod loader for versions 1.1+ of Minecraft.
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DeepMobLearningReloaded
DeepMobLearningReloaded is a mod based on Deep Mob Learning: Reforged with the corresponding data model added.
Added data model Mob.
- Elder Guardian
- Evoker
- Hogrin
- Magma Cube
- Phantom
- Piglin
- Ravager
- Warden
Total of 21 data models
The list of mobs that can be trained on the data model can be viewed by pressing "Ctrl" with the mouse over the data model.
MOD Collaboration
DeepMobLearningReloaded collaborates with several MODs, and the collaboration is divided into different levels.
Level 1:
- Addition of target mobs for existing data models
- Addition of monster drop items producible with pristine matter
Level2:
- Data models for mobs introduced by the target MOD
- Pristine matter associated with the target MOD
Level1
Compatible MODs for Level 1 Collaboration:
Level 2
Compatible MODs for Level 2 Collaboration:
Other
- Recipe Display: Just Enough Items (JEI)
- Addition of dedicated slots for the learning device: Curios API (Forge)
Reference MOD
Original MOD description
This mod however uses "Data models" that you train by defeating monsters both by hand or by simulation(In the simulation chamber).
To get started you will need a Deep Learner, which will house the data models, and some type of mob data model.
Once your data models have reached higher tiers you can use them in the Simulation Chamber to get "Transmutational" matter, you'll get different ones depending on which type the Data Model is. multiple Data Models can share the same type.
Overworld mobs -> Overworldian matter Nether mobs -> Hellish matter End mobs -> Extraterrestrial matter
These can be used to transmute items that come from that corresponding dimension/origin.
License
This project is licensed under the MIT License.
DeepMobLearningReloaded
DeepMobLearningReloaded is a mod based on Deep Mob Learning: Reforged with the corresponding data model added.
Added data model Mob.
- Elder Guardian
- Evoker
- Hogrin
- Magma Cube
- Phantom
- Piglin
- Ravager
- Warden
Total of 21 data models
The list of mobs that can be trained on the data model can be viewed by pressing "Ctrl" with the mouse over the data model.
MOD Collaboration
DeepMobLearningReloaded collaborates with several MODs, and the collaboration is divided into different levels.
Level 1:
- Addition of target mobs for existing data models
- Addition of monster drop items producible with pristine matter
Level2:
- Data models for mobs introduced by the target MOD
- Pristine matter associated with the target MOD
Level1
Compatible MODs for Level 1 Collaboration:
Level 2
Compatible MODs for Level 2 Collaboration:
Other
- Recipe Display: Just Enough Items (JEI)
- Addition of dedicated slots for the learning device: Curios API (Forge)
Reference MOD
Original MOD description
This mod however uses "Data models" that you train by defeating monsters both by hand or by simulation(In the simulation chamber).
To get started you will need a Deep Learner, which will house the data models, and some type of mob data model.
Once your data models have reached higher tiers you can use them in the Simulation Chamber to get "Transmutational" matter, you'll get different ones depending on which type the Data Model is. multiple Data Models can share the same type.
Overworld mobs -> Overworldian matter Nether mobs -> Hellish matter End mobs -> Extraterrestrial matter
These can be used to transmute items that come from that corresponding dimension/origin.
License
This project is licensed under the MIT License.
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