Some feedback: - Knowing the scoring system is helpful when going 1v1 high score
- Use a different randomization system, I kept getting starved for pieces like I. True random is fine, throwing a copy of every piece into a bag and then drawing them one by one is better (7 bag), nearly random with some lookbehind to prevent getting a string of ZSZS is solid, too (TGM randomizer)
- Piece rotation feels left-biased, and keeps making me mis-drop, like the T pieces shift to the left if you spin 4 times. Check out https://tetris.wiki/images/thumb/3/3d/SRS-pieces.png/300px-S... or https://tetris.wiki/images/b/b5/Tgm_basic_ars_description.pn... for examples of how other games are doing it.
- Clockwise and counter-clockwise rotation is important for human players, we can only hit so many keys per second
- re-mappable keys are also appreciated
Nice work, I'm going to keep watching.
I don't think the goal is to make a PvP simulator, it would be too easy to cheese or do weird strategies. It's mostly for LLMs to play.
On the topic of reflexes decaying (I'm getting there, in my late 30s): Have you played Stackflow? It's a number go up roguelite disguised as an arcade brick stacking game, but the gravity is low enough that it is effectively turn based. More about 'deck' building, less about chaining PCs and C-Spins.
One of my dream goals was to make a licensed low lag competitive game kind of like TGM, but I heard licensing is extremely cost-prohibitive so I kind of gave up on that goal. I remember I said to someone I was ready to pony up few tens of thousands for a license + cut, but reportedly it starts at an order of the magnitude higher.
- Each model starts with an initial optimization function for evaluating Tetris moves.
- As the game progresses, the model sees the current board state and updates its algorithm—adapting its strategy based on how the game is evolving.
- The model continuously refines its optimizer. It decides when it needs to re-evaluate and when it should implement the next optimization function
- The model generates updated code, executes it to score all placements, and picks the best move.
- The reason I reframed this problem to a coding problem is Tetris is an optimization game in nature. At first I did try asking LLMs where to place each piece at every turn but models are just terrible at visual reasoning. What LLMs great at though is coding.
Also, if the creator is reading this, you should know that Tetris Holdings is extremely aggressive with their trademark enforcement.
Curious what the latency looks like per move. That seems like the actual bottleneck here.
A youtuber sentdex has a whole series on parsing the game's image and playing GTA https://www.youtube.com/playlist?list=PLQVvvaa0QuDeETZEOy4Vd...
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It will lose so badly there will be no point in the comparison.
Besides you could compare models (and harnesses) directly against eachother.
I mean, if you let the LLM build a testris bot, it would be 1000x better than what the LLMs are doing. So yes, it is fun to win against an AI, but to be fair against such processing power, you should not be able to win. It is only possible because LLMs are not built for such tasks.
Task: write and optimize a tetris bot
Task: write and safely online optimize a tetris bot with consideration for cost to converge
openai/baselines (7 years ago) was leading on RL and then AlphaZero and Self-Attention Transformer networks.
LLMs are trained with RL, but aren't general purpose game theoretic RL agents?
"Outsmarting algorithms: A comparative battle between Reinforcement Learning and heuristics in Atari Tetris" (2025) https://dl.acm.org/doi/10.1016/j.eswa.2025.127251
That difference in objective bias shows up very clearly in Tetris, but is much harder to notice in typical coding benchmarks. Just a theory though based on reviewing game results and logs
