● Covered by 1 source · 1 reportLow impact1 neutral
AI World Model Trained to Select Starter Pokémon in Pokémon Red
Key points
- A world model based on JEPA architecture was used.
- The model was trained to select a starter Pokémon in Pokémon Red.
- The task involved planning a sequence of button presses.
- The experiment faced unexpected difficulties during training.
AI World Model Application
An AI world model, specifically a variant of Yann LeCun's Joint Embedding Predictive Architecture (JEPA) called LeWorldModel1, was utilized for an experimental task. The model was chosen due to its smaller size, allowing for local training on an RTX 3080 Ti, and its simpler design compared to previous JEPA architectures.
Pokémon Red as a Training Environment
The 1996 game Pokémon Red was selected as the environment for the world model. Pokémon Red is a game where players explore a world, collect creatures, and engage in battles. The game mechanics involve using directional buttons for movement and an 'A' button for interaction and confirmation, while a 'B' button is used for canceling actions.
Task Definition and Execution
The initial goal for the model was to navigate to Professor Oak's lab, complete dialogue, select a starter Pokémon, attempt to exit the lab, and then face Professor Oak's grandson. This ambitious plan was narrowed down to the specific task of selecting a starter Pokémon from a saved game state. The model successfully planned a sequence of button presses to select Squirtle, despite encountering more difficulties during training than anticipated from simpler tests.
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Reporting from
An AI world model, based on a variant of Yann LeCun's JEPA architecture, was trained to perform the task of selecting a starter Pokémon in the game Pokémon Red. This experiment demonstrates the application of world models to learn and execute specific actions within a simulated environment, highlighting the practical challenges in training such models for even seemingly simple tasks.