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so in the next demo i will present spike that weve done and that is the reinforcement learning in the resolution process let me share my screen and i prepared few slides just to give an overview so uh as of now we are using gradient three methods that is temporal difference learning and monte carlo research monte carlo 3 learning during the resolution process so we know how to resolve software stacks that have high quality given data and observations that we have but what if we plug a deep reinforcement learning algorithm so lets take a look at gradient methods and how they could be utilized during the resolution process just to give some context here uh you probably already know uh these uh this plot but basically uh what we are doing we are scoring software stacks so if a software step is made out of two libraries one is called simply and the other one is another leap and we switch versions of these libraries and score uh them we can get this interpolated surface that states score
