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Multi-Task Learning in Atari Video Games with Emergent Tangled Program Graphs

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Re: Multi-Task Learning in Atari Video Games with Emergent Tangled Program Graphs

#31

Post by [deleted] »

desku wrote: post #20
raverbashing wrote: post #15

Earlier quoted context omitted.

Well, the accrual of "useless code" (there's a name for this that I forgot") is a known problem, but it is also something that stabilizes the learning process I don't think it's as simple as putting the length of the AST in the goal function (but it's something interesting to try). Depending on compile speed vs running speed you might be better off interpreting your ASTs

It's bloat due to 'introns' (useless statements that don't effect the output, like x = x * 1). And yes, just adding a fitness function to shorten program length isn't optimal. I've found it easier to evolve successful programs (letting the bloat happen) and then keep removing statements from correctly generated programs whilst checking if the output is the same. Probably not optimal either but I feel like it gives be…

[deleted]

Re: Multi-Task Learning in Atari Video Games with Emergent Tangled Program Graphs

#32

Post by honestoHeminway »

Genetic Programming is cool and a very important tool - but it inherits the flaws of evolution as a process. It can get caught in local optima. It suffers, from there beeing no it, as in there is nothing home, that can recombine approaches into a process. A NN is not able to develop a plan- but it is able to capture a state-machine of approaches to take, and recombine these approaches.

Re: Multi-Task Learning in Atari Video Games with Emergent Tangled Program Graphs

#36

Post by [deleted] »

symmetricsaurus wrote: post #24
bomdo wrote: post #17

I was a little surprised at the headline, since I expected 'outperforms' to mean that it had better end-results, which is of course not the case. GP is just much faster due to it's relative simplicity and the results are close enough to those achieved with NN and deep learning. > Finally, while generally matching the skill level of controllers from neuro-evolution/deep learning, the genetic programming solutions evol…

> I was a little surprised at the headline, since I expected 'outperforms' to mean that it had better end-results, which is of course not the case. GP is just much faster due to it's relative simplicity and the results are close enough to those achieved with NN and deep learning. From figure 3 in the paper it seems like it outperforms DQN on all games but one. So, it has better end results as well. Edit: There are ot…

[deleted]

Re: Multi-Task Learning in Atari Video Games with Emergent Tangled Program Graphs

#38

Post by sk0g »

posterboy wrote: post #23
phoe-krk wrote: post #10

Earlier quoted context omitted.

What a time to live in, when papers from 2015 are "really old" in 2017.

What is "exponential growth"!

It's more logarithmic, isn't it? Right now we're at the beginning stage, where there's massive discoveries and changes happening, but in say, 50 years time, there won't be much changing year-to-year.

Re: Multi-Task Learning in Atari Video Games with Emergent Tangled Program Graphs

#40

Post by adrianratnapala »

desku wrote: post #20
raverbashing wrote: post #15

Earlier quoted context omitted.

Well, the accrual of "useless code" (there's a name for this that I forgot") is a known problem, but it is also something that stabilizes the learning process I don't think it's as simple as putting the length of the AST in the goal function (but it's something interesting to try). Depending on compile speed vs running speed you might be better off interpreting your ASTs

It's bloat due to 'introns' (useless statements that don't effect the output, like x = x * 1). And yes, just adding a fitness function to shorten program length isn't optimal. I've found it easier to evolve successful programs (letting the bloat happen) and then keep removing statements from correctly generated programs whilst checking if the output is the same. Probably not optimal either but I feel like it gives be…

I guess a lot of standard compiler optimisation techniques could be used here -- if you care.

I don't know what that would do to the learning process -- but at least it would be useful for end results.

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