TL;DR at the bottom. Hello there guys. I will try to keep this as short as possible. I have been coding my own genetic algorithm from scratch for some experiments and, mostly, for fun. I am no expert in AI or anything, I am in fact a Computer Science Bachelor dropout, so I would appreciate if we could keep this as simple as possible. I want to take it a step further and code the simplest possible PPO for this project, and I think I have some vague idea of how to approach this, but a lot of questions arise. How does a PPO save it’s knowledge about the environment? Like it has X input neurons and Y output neurons, but how does it save, in disk, the output that is the most appropriate for each case? Does it need a huge database for every possible combination of environment variables so it can weight it’s options? I understand that is what weights are for, but how does it keep track of the relationships between a given set of activated input neurons, output neurons, desired result and actual result so the weight can be applied? If the answer is gonna be a math formula, can you instead tell me in one sentence what exactly is that formula doing? The step from the genetic algorithm to the PPO feels like a huge leap to me. Thank you in advance friends!
*TL;DR : How does a PPO keep track of the relationships between input neurons, output neurons, weights, desired result and actual result? submitted by /u/Useful_Researcher_79
Originally posted by u/Useful_Researcher_79 on r/ArtificialInteligence
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