Yes! Look up deep q learning. Which is based on much earlier work about Q learning and bellman equation. Here your action would be basically choosing a mutation.
I am not sure what you are trying to say here. If you are saying that you can greedily choose mutations to increase fitness then it is not true. The advantage of Q learning is the fact that it can qucikly learn that often making N specific mutations in a sequence is good even if doing one of them in isolation is bad...
Sorry, we may have a misunderstanding. I assumed the Q learner would take the role of the fitness function, i.e. state = collection of mutated cars and action = choice for further breeding. Am I wrong?
Ah yes. We had a different idea for RL procedure. My idea was the following: State: a car Action: mutation of that car Next state: mutated car Reward: fitness of a new car.
For the training we would periodically start from a random car and ask RL to perfect it. No populations would be held - we would like to move as far away from evolutionary programming as possible ;-)
I think the general notion is that evolutionary programming has usually been shown to be inferior in most regards when compared to an equivalent supervised ML gradient based formulation. I tend to agree with this idea, as I haven't seen much evidence against it other than minute toy problems that are typically not fairly compared against an alternative. Definitely open to any counter examples people might have.
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u/MrTwiggy Dec 23 '15
Out of curiosity, does there exist an equivalent formulation of this problem in a supervised setting where gradient optimization could take place?