A tiny world model running games on a Raspberry Pi Pico 2 W
READ THE ORIGINAL SOURCEOpen original sourcegithub.com · Opens in a new tabThe tinyworldformer project uses two small neural networks on a Raspberry Pi Pico 2 W: a world model that predicts the next frame and reward from the current frame and action, and an actor that picks actions from the generated frame. The world model is a looped transformer, reusing one block several times with shared weights to save memory. The author used small games (such as Breakout, Pong, Flappy-style and a DOOM-lite variant) whose real versions were written in numpy to check generated frames. A streaming RL agent was later added so the board can learn to play inside its own generated dream.
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The tinyworldformer project uses two small neural networks on a Raspberry Pi Pico 2 W: a world model that predicts the next frame and reward from the current frame and action, and an actor that picks actions from the generated frame. The world model is a looped transformer, reusing one block several times with shared weights to save memory. The author used small games (such as Breakout, Pong, Flappy-style and a DOOM-lite variant) whose real versions were written in numpy to check generated frames. A streaming RL agent was later added so the board can learn to play inside its own generated dream.
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Key findings from the stored brief
- The world model is a looped transformer that applies one block repeatedly with the same weights.
- One network generates the game frames and rewards; a second network plays using those generated frames.
- Games used include Breakout, Pong, Flappy-style and a DOOM-lite variant, shown as dreamed gameplay.
- A streaming RL agent was added so the microcontroller can learn to play inside its own generated dream.
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Why this may matter
It shows that learned game-simulation models can be compressed to run on very low-cost microcontrollers, relevant to on-device world models and embedded reinforcement learning.
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