| This is an awesome overview and if you want more, most of those are documented in an approachable way on YouTube. Just wanted to provide some perspective here on how many things those projects need to take care of in order to get some training setup going. I'm the developer behind TMInterface [1] mentioned in this post, which is a TAS tool for the older TrackMania game (Nations Forever). For Linesight (last project in this post), I recently ended up working with its developers to provide them the APIs they need to access from the game. There's a lot of things RL projects usually want to do: speed up the game (one of the most important), deterministically control the vehicle, get simulation information, navigate menus, skip cut scenes, make save states, capture screenshots etc. Having each of those things implemented natively greatly impacts the stability and performance of training/inference in a RL agent, e.g. for the latest version the project uses a direct capture of the surface that's rendered to the game window, instead of using an external Python library (DxCam). This is faster, doesn't require any additional setup and also allows for training even if the game window is completely occluded by other windows. There are also many other smaller annoying things: many games throttle FPS if the window is unfocused which is also the case here, and the tool patches out this behaviour for the project, and there's a lot more things like this. The newest release of Linesight V3 [2] can reliably approach world records and it's being trained & experimented with by quite a few people. The developers made it easy to setup and documented a lot of the process [3]. [1] https://donadigo.com/tminterface/ [2] https://youtu.be/cUojVsCJ51I [3] https://linesight-rl.github.io/linesight/build/html/ |