9/27/2023 0 Comments Qwop running game flash game![]() There are multiple ways of achieving this, for instance training another neural net In order to get the score in this setup, pixel data on top of the screen (see picture 1) must be interpreted as text. Reinforcement learning is all about rewards, rewards are based on the game scoring system, in the QWOP case the distance score should somehow be related to distance and if possible time and running style. With mss taking the screenshot, frames were taken every ~0.006s (166 FPS!!). The only issue is that I had to hard code the position of the game in my screen hence ruining the ability to reliably reproducing the code (less than a frame per second), consider that 60 FPS is approximately 0.016 ms between each frame.Īfter discarding this method, taking a screenshot using the mss library seems to be the more sensible choice. Ways such as using the native selenium take_screenshot which turns out to be extremely slow, in the order of 1.6 ms between each frame Obtaining the observation, that is, the raw pixels of the current frame of the game can be accomplished in several With the game running on demand and with less delay, the environment should have the ability to return at least,Īnd as is usual in openAIs gym environments, an observation, a score, and a way to determine if the current game swf (flash format) can be embedded in a browser testing environment, like selenium, after setting some obscure chrome flags that enable playing flash multimedia. That runs the game (read: I didn't try hard enough), and instead had to cope with the flash player version. Unfortunately, I found no way of getting the javascript code Without the need to make requests to the official site that hosts the game. The first thing is figuring out a way to run the game reliably Inspired by this video, the following is an attempt to code an agent using reinforcement learning that reaches consistently the 100 meter mark with under 8 hours of training.īefore we dig into the approach, here’s what I found to be the most useful writeup about getting started with reinforcement learning and an example of what is used in this project.īesides that here this are some useful resources to get started with RL.Īs usual, some leg work is required in order to have a runnable environment. As simple as it might seem, reaching the 100 meter mark took Mike approximately 8 hours. On one of this challenges, he tries to learn QWOP ( )Ī game (writen by Bennett Foddy) in which there are four input buttons Q,W,O,P that control tighs and calves of an infuriating To learn skills like juggling, stacking dice, and others while recording himself until he / Mike, as he likes to put it, is an average guy that tries Have you watched Mike Boyd’s channel on youtube?.
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