In the video, Dave demonstrates his “vibe coding” approach by live-coding an AI to play Tempest through trial and error, using multiple game instances on a powerful machine to gather game state data and iteratively refine a reward function. He emphasizes simplifying the reward structure, adjusting parameters to guide the AI’s behavior, and efficiently retraining the model to improve gameplay in real-time.
In the video, Dave introduces his process of “vibe coding,” where he narrates his live coding session rather than writing a story afterward. He begins by adjusting his coding environment, specifically increasing the font size for better readability. The main focus of his project is training an AI to play the game Tempest through trial and error, with the AI gradually improving its performance by learning from game states and rewards.
Dave explains the setup of his system, which involves multiple instances of Tempest running on a powerful Thread Ripper machine with 96 cores and 512 GB of RAM. These instances report their game states to a master AI server, which processes snapshots of the game every few frames. The game state data includes information about levels, lives, enemies, shots, and various other game elements, which are extracted using a script. He mentions that he currently passes around 200 out of 350 extracted data points to simplify the model and speed up learning.
He then delves into the design of his reward function, which scores the AI’s performance based on its actions and game outcomes. The reward system penalizes risky behaviors like using the super zapper unnecessarily and rewards good evasive maneuvers, successful hits, and strategic positioning. Dave discusses how he tweaks these reward parameters, such as increasing penalties for certain actions or adjusting the importance of specific game states, to guide the AI toward better gameplay. He also notes the challenge of testing these changes, as running enough game frames to validate improvements takes significant time.
Throughout the coding process, Dave modifies various parts of the reward function, including how the AI responds to enemies, shots, and level elements like pulsars and fuse balls. He emphasizes simplifying the reward structure by removing unnecessary complexity, such as proximity scaling for certain threats, to make the AI’s learning process more straightforward. He also explains how the AI is programmed to avoid dangerous situations, like moving into pulsar lanes or charging fuse balls, by assigning appropriate rewards or penalties based on the game state.
Finally, Dave demonstrates how he resets and retrains the AI by killing previous processes, clearing data, and launching new instances on his powerful machine. He shows the setup of multiple Tempest instances running simultaneously, achieving high frame rates that facilitate rapid training. Concluding the session, he thanks his hardware provider and invites viewers to subscribe and comment if they enjoy this type of content. The video offers an inside look at live AI training, game state analysis, and iterative reward tuning in a real-time coding environment.