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Multi-Task Learning in Atari Video Games with Emergent Tangled Program Graphs
- nocoder 9y agoThis sounds interesting. I will like someone from the field of genetic programming on how this works and how it differs from current DL approaches.
- henning 9y agoKelly's approach involves evolving teams of programs. His basic strategy is to have a scalable problem decomposition strategy. So programs that process pixels and the teaming of those programs are grouped together. The groupings (teams) themselves are co-evolved with the programs, simultaneously. This enables niching and specialization behavior. This builds on earlier work on 'symbiotic bid-based genetic programming' from other people at Dalhousie, the same university Kelly is at. The innovation of this paper is that teams can reference other teams. This allows for the creation of hierarchical teams. (There are rules to prevent cycles and other edge cases.) Everyone commenting here is probably going to just look at numerical game score and ignore the fact that the runtime performance of Kelly's tangled program graphs. They are 1000 times smaller than a deep neural network. That matters for things like running on mobile/embedded devices.
- Normal_gaussian 9y ago> That matters for things like running on mobile/embedded devices. Ding ding ding. This is where the money is at, good yet cheap sensors that sense human level actions are needed for IoT to be impactful.
- nocoder 9y agoThis sounds like a divide & conquer approach (Sorry if this generalization is too lame). If it can work on less capable device than it will create a new wave of innovations in mobile devices. I am wondering, whether a similar approach is possible with current DL models and will it have any performance improvements over what is existing or whether it will be computationally even more expensive.
- posterboy 9y agoIn my rough understanding, DNN is genetic programming i a sense, matrixes over a vectorfield can be thought of as operators, and neurons are layers of xor circuits ...
- nivwusquorum 9y agoThose are really old results. They should compare to this one: https://arxiv.org/pdf/1511.06581.pdf https://arxiv.org/pdf/1511.06581.pdf
- higgsfield 9y agothis is old too
- csl 9y agoHow can the results be old when the paper is from 2017?
- aqsalose 9y agoThey are comparing their genetic programming results with a deep learning paper published in 2015. [1] [1] https://www.nature.com/nature/journal/v518/n7540/abs/nature14236.html https://www.nature.com/nature/journal/v518/n7540/abs/nature1...
- partycoder 9y agoThe convenient thing about Atari games is that there is usually a numerical score that can be used as input for the fitness function.
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- smdz 9y agoOne of the huge benefits of GPs over NNs is the ease of reverse engineering a GP tree compared to NN models. Its not effortless however. Its just not mathematically complex like NNs i.e. a programmer who isn't a mathematician can analyze GPs with a lot of patience EDIT: I have found GPs to be relatively slow-to-very-slow. But very likely that is because of the lack of interest and development compared to NNs
- aerique 9y agoPartly, I think they're also fundamentally slower than NN because you're manipulating and executing programs (ASTs[1]) while for NNs you just adjust some values. I've dabbled in GP and I really like it but those ASTs can get huge if they're not carefully pruned and might not add to the solution at all. [1] https://en.wikipedia.org/wiki/Abstract_syntax_tree https://en.wikipedia.org/wiki/Abstract_syntax_tree
- vengarioth 9y agoI don't think you can generalize it this way, ASTs could be compiled to fast machine code, also it really depends on the solutions the algorithms come up with. The NN is bound to its number of parameters, while the Genetic's program varies in length and can become quite small if length is part of the fitness function.
- raverbashing 9y agoWell, 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
- desku 9y agoIt'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 better results.
- bomdo 9y agoI 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 evolved here are several orders of magnitude simpler, resulting in real-time operation at a fraction of the cost. > Moreover, TPG solutions are particularly elegant, thus supporting real-time operation without specialized hardware This is the key takeaway and yet another reminder to not make deep learning the hammer for all your fuzzy problems.
- symmetricsaurus 9y ago> 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 other results linked in this thread that are better than the 2015 DQN results that the paper refers to.
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- cshenton 9y agoThis is super cool, but it doesn't outperform deep learning based RL methods. In fact, I'm not sure how much more compute efficient than something like A3C it would be. That can produce 4x the score of DQN in a comparable number of hours (and on a CPU).
- habitue 9y agoA3C is only ever run on one game at a time[0]. This paper gets good performance on all games with the same agent [0] read as: I have only seen papers with 1 agent per game for A3C
- cshenton 9y agoSo it can train on one game and play without training on a previously unseen (but also atari) game? That's pretty neat, DQN and A3C certainly can't do that.
- habitue 9y agoNo, in this case it is trained on all the games, but retains good scores on all of them. If you train basic AC3 in all the games, you'll get poor performance on all the games due to catastrophic forgetting
- honestoHeminway 9y agoGenetic 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.
- vivek1410 9y agoabcd
- kyberias 9y agoI don't think genetic algorithms are allowed here.
- vivek1410 9y agotesting
- 99mistakes 9y agoSlightly relevant, here's a state of the art drone AI built using genetic fuzzy systems: https://www.forbes.com/sites/jvchamary/2016/06/28/ai-drone/#50908d8b7081 https://www.forbes.com/sites/jvchamary/2016/06/28/ai-drone/#...
- gourou 9y agoGenetic Programming seems lightweight, what are some cool applications they have?
- criddell 9y agoIt's just a toy, but it's pretty entertaining to watch: http://boxcar2d.com/ http://boxcar2d.com/ Unfortunately, it requires Flash.
- taw55 9y agoA couple of years ago I read about some printer manufacturer evolving the shapes of their nozzles. Apparently the problem was nontrivial and rather than doing complicated analyses up front they found it more efficient to simply generate and fabricate random permutations of shapes and evolve them over many generations. The results were better than any human designed ones, apparently.
- hchasestevens 9y agoThere are actually lots of very exciting GP applications! One of my favorites is "Fixing 55 out of 105 bugs for $8 each", in which GP is used to automatically repair code: https://www.cs.virginia.edu/~weimer/p/weimer-icse2012-genprog-preprint.pdf https://www.cs.virginia.edu/~weimer/p/weimer-icse2012-genpro... . They've also achieved better-than-human level results in antenna design for NASA ( https://ti.arc.nasa.gov/m/pub-archive/1244h/1244%20(Hornby).pdf https://ti.arc.nasa.gov/m/pub-archive/1244h/1244%20(Hornby).... ) and in discovering novel quantum computing algorithms ( http://faculty.hampshire.edu/lspector/pubs/GP-quantum-GP98-with-cite.pdf http://faculty.hampshire.edu/lspector/pubs/GP-quantum-GP98-w... ). I'll also shamelessly hock here my GP framework for Python, in case you're interested in experimenting: https://github.com/hchasestevens/monkeys https://github.com/hchasestevens/monkeys
- gourou 9y agoWhat's a good starting point for someone interested in building game AI?
- taw55 9y agoI'm far from knowledgable in the field, but I did a survey some time ago and think these items should provide a decent basis: Dijkstra and A* Pathfinding, Finite State Machines, Decision Trees, Hierarchical Task Networks (SHOP, etc) Keep in mind that game ai algorithms are all about decision taking, there's little "intelligence" involved, unlike the broader aim of "general" ai.
- tdb7893 9y agoCurrent game ai is vastly different than this I think. I think there is a good writeup on the ai from FEAR that might be a decent read
- uoaei 9y agohttp://alumni.media.mit.edu/~jorkin/goap.html http://alumni.media.mit.edu/~jorkin/goap.html
- desku 9y agogameaibook.org
- jerianasmith 9y agoI like GP, but the problem is AST. These can get huge. But the only advantage is ease of reverse engineering