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> A key feature of neural networks is that the outputs are a nonlinear function of the inputs. However, steering a 2D car is simple enough that a linear functio
by stfwn 6y ago
> A key feature of neural networks is that the outputs are a nonlinear function of the inputs. However, steering a 2D car is simple enough that a linear function is more than sufficient, and neural networks are unnecessary.
This depends entirely on the definition of 'steering a 2D car'. In the model used, throttle is simply proportional to the distance to the nearest wall in front of the car. This means the agent will never accelerate coming out of a corner, because it can't know it has the headroom to steer away from the wall as it's coming out.
Similarly, the model for steering inherently steers the car towards the middle of the track. I would expect the car to wobble from left to right if the road's edges are ragged, make up its own corners if the track edges describe a 'fake' turn on a straight bit, and the car would likely crash if it were to encounter a Y junction or a pit stop. The neural network agents showed smarter behavior here because it is able to capture more complex cross-dependence between different inputs.
On the topic of junctions, if the track were to include them, perhaps it'd be nice if the car chose the quickest route to optimize for lap times. But maybe that stretches the problem statement too much.
> Instead of doing anything fancy, my program generates the coefficients at random to explore the space. If I wanted to generate a good driver for a course, I’d run a few thousand of these and pick the coefficients that complete the course in the shortest time.
In theory this is more random and less efficient than an evolutionary algorithm, which searches the problem space in a structured way. If the author really wanted to hammer the point home, a least squares method to one-shot the coefficients would be more convincing.
All in all, the author doesn't make any hard claims that are false. But I would nuance the point of "neural networks are unnecessary" to "simpler models will do for simpler objectives".
- Der_Einzige 6y agoEven more interesting is to show that you can also optimize the neural network weights using a GA or even the authors own basic method. Would be interesting to compare the results of the authors method with the neural network optimized in the same way.
- m463 6y agoin motorcycles (and rwd cars) everyone knows you steer with the rear anyway.
- tyingq 6y agoSomewhat related, it's interesting how non-intuitive steering is to people for motorcycles and bicycles. They do it correctly, but it's hard to reason about. That is, that pushing the left handgrip forward, at speed, turns left and not right. Yet, at very slow speeds, like walking it, it's the opposite.
- CraigJPerry 6y ago>> pushing the left handgrip forward, at speed, turns left and not right This is the most counter intuitive physics i’ve ever experienced. The really weird thing is i’d been riding pushbikes all my life and had my motorbike license almost a year before i learned this. It wasn’t taught as part of the licence training. Super handy to know as a tool, it’s kept me from going for a closer look at the scenery on a couple of occasions.
- cptnapalm 6y agoYou should try a sidecar. Want to go left, turn left. Want to go right, turn right, but not too fast because the chair takes to the air and now motorcycle physics are involved.
- thakoppno 6y agothat reminds me of a street hustle in london where a guy had reversed the steering mechanism and would bet you a pound you couldnt steer it straight 5 meters. pretty interesting to me that we can reverse our intuition.
- muststopmyths 6y agoAfter a few years of riding motorcycles I once went on my first snowmobile ride. The controls were so close to a bike that I kept countersteering into the side of the trail. Once I figured out what I was doing wrong, I had great fun from then on. The other interesting thing is how hard it is to convince non-riders that counter-steering is a thing. They will just not believe you. Even people who've grown up riding bicycles and counter-steering unconsciously their entire childhood.
- blackbrokkoli 6y agoI see the article more as a broader metaphor for the AI hype. Take, I dunno, video recommendation. Sure, YouTube itself proably built insane stuff in their engine you could never replicate with classic methods (ignoring whether the YT algo is any good). However, if we are just talking about the Vlog of your real estate company, you should probably A/B-test whether your viewers prefer order by time or clicks and implement a decent title search bar. And kick the consultant hyping you up about ML out, now. So my takeaway is that is not about whether AI is never useful or about 2D steering, but about using the right tool for the right job. And building on that, I have to give the author probs to demonstrating an alternative solution to I problem wich I would have definitely solved via AI.
- mumblemumble 6y agoVideo recommendation is the quintessential machine learning killjoy. YouTube and Netflix were a lot more interesting before they achieved algorithmic homogeneity.
- finnthehuman 6y agoThey kill exposure to anything fresh, you teach it a couple things you like and then it keeps you swimming in the same pool. Rather than discovering something new, everyone just watches The Office and Parks and Rec., again and again. Now those theme songs make my skin fucking crawl.
- galangalalgol 6y agoOn the other hand I taught pandora to only play songs by artists that had done heroin, thats kind of cool, it has tons of variety from ray charles to jhonny cash to alice in chains, and it finds artists I had no idea about like James Taylor. Also, don't try to code while listening to my horses channel... There may be variety but there is also a common quality of alert-sedation.
- blackbear_ 6y ago> ... and then it keeps you swimming in the same pool. This is a consequence of the metrics that are being optimized, it's not a fault of the algorithm per se.
- bluGill 6y agoThere is enough delay in the feedback loops that the simple steer to the middle works - despite the jagged edge, you still track a straight line to within a few cm.
- noobermin 6y agoThat is the point, for some problems NN is unnecessary. That isn't an argument against the point, that is the point. Many problems in life allow simpler models and can save time and computational power if people used their brains first.