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Training an unbeatable AI in Trackmania

Yosh
Explainers
YouTube
1 HN user recommended this explainer1 month2024–2025 score 1.5
Years with an HN recommendation, 2007–2026

Recommended as an approachable explanation of how reinforcement learning works in practice, and cited for the constraints needed to stop exploits.

Yosh trains a reinforcement learning agent to drive Trackmania until he can no longer beat it. The video explains how the training works and the design choices involved.

“be sure to check out the videos by Yosh. They're amazing, and do a great job explaining how reinforcement learning works in practice” (HN, 2024)

1 strong recommendations · 3 citations · Explainers · intro

4 Hacker News comments

From the HN archive; up to 20 kept per video, favoring different commenters. Recommendations first.

@smokel Jul 2024 Strongly recommends
on HN thread The History of Machine Learning in Trackmania
For those who only read the comments, be sure to check out the videos by Yosh. They're amazing, and do a great job explaining how reinforcement learning works in practice: [this video] [link]
Amazing explanations of how RL works in practice

Citations, criticism and jokes

@emporas Jan 2024 Cites it
on HN thread Chess-GPT's Internal World Model
Read more
>The entire point of this is to not encode the board state! I am not sure about this. From the article "The 50M parameter model played at 1300 ELO with 99.8% of its moves being legal within one day of training." I thought that the experiment was how well the model will perform, given that it's reward function is to predict text, rather than checkmate. Leela, Alpha0 their reward function is to win the game, checkmate or capture pieces. Also it goes without saying that Leela, Alpha0 cannot make illegal moves. The experiment does not need to include the whole board position if that's a problem, if that's an important point of interest. It could encode more information about squares covered by each side for example. See for example this training experiment for Trackmania [1]. There are techniques that the ML algorithm will *never* figure out by itself if this information is not encoded in it's training data. The point still stands. PGN notation certainly is not a good format if the goal (or one of the goals) of the experiment is to be a good chess player. [1] [this video]
Example of encoding extra info in RL training
@fragmede Oct 2024 Cites it
on HN thread OmniParser for Pure Vision Based GUI Agent
Depends what you consider fun, and how far you take it. Some people enjoy programming more than repetitive clicking in a GUI. For a clicker game, writing a bot lets you iterate on strategies easier - is it faster to get to level 2 if I buy the upgrade for A or B first? For Trackmania, it lets you get a world record and a YouTube video with 14M views. [this video]
@szvsw Feb 2025 Cites it
on HN thread AI-designed chips are so weird that 'humans cannot understand them'
There’s a few very cool examples where someone recently used RL to solve trackmania, and ends up having to add all sorts of constraints/penalties to prevent extremely strange exploits/glitches that are discovered IIRC… been a while since I watched. [this video] [link]
RL on Trackmania needed constraints to prevent glitch exploits

Video list and Hacker News comment labels from HackerTube (prompted by @karpathy), released under ODC-By 1.0, built on the open-index/hacker-news archive. Hacker News data is provided by Y Combinator; comments remain the work of their authors. Video titles, channel names and thumbnails belong to their creators and are shown via YouTube. HackerTube Watch is an unofficial edition. Data export of 2026-10-31.

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