Hacker News Reader

Comment by deepnet | Hacker News Reader

[accompanying blog post](http://blog.otoro.net/2015/03/28/neural-slime-volleyball/)

I too am perplexed as to what the neurons represent, the blog describes the RNN as 19 inputs connected to 7 outputs.

Examining slimevolley_pro.js (Agent.prototype.drawstate) shows only the agent states are depicted onscreen x,y,vx,vy,bx,by,bvx,bvy in the top row and actions forward, jump and backward below.

The code is well documented and builds on convnet.js by Karpathy.

The network is trained by genetic algorithm and self play - so this is a neural net trained with reinforcement learning.

The author's method seems unique and effective.

The blog post comparison of the resultant 'genomes' of network weights seem to show that the space has been searched exhaustively.

The game is played with trained networks but the code contains a training flag - it will be interesting to watch them train with self play.

View on HN

It's just an abbreviated form of the nets shown in this (http://blog.otoro.net/wp-content/uploads/sites/2/2015/03/sli...) image, the 3 outputs displayed correspond to the "forward", "jump", and "back" on the diagram, and the 8 inputs correspond to the agents' and balls' position vectors (which you can see in the Brain() code in slimevolley_pro.js).

The method is not unique. See some of the work of Schmidhuber's group. They are doing a lot of reinforcement learning for recurrent nets (LSTMs) and also via evolutionary algorithms. Eg see Evolino.

I stand corrected, Schmidhuber in AMA http://www.reddit.com/r/MachineLearning/comments/2xcyrl/i_am... has an evolved Atari agent before Mnih

Otoro states he is using CNE Conventional Neural Evolution, but combining it with ideas about recurrence from the Atari paper.

http://blog.otoro.net/2015/01/27/neuroevolution-algorithms/

He outlines the evolution of his thinking and slime volleyball in this post which cites John Gomez's thesis as the inception of CNE.

Certainly parallel ideas to Schmidhuber but the implementation details are somewhat different in the U of Texas Neuro-Evolution models.

None of the ideas are new.

http://people.idsia.ch/~juergen/evolino.html

Yet Schmidhuber's nets are much more complex and certainly different.

I still think Otoro's very simple RNN feedback nets are unique - especially when coupled with training by self-play.

Does Schmidhuber have any game playing agents ?

I was very impressed with this handwriting demo (and the accompaning paper):

http://www.cs.toronto.edu/~graves/handwriting.html

Using a recurrent architecture.

To be honest I haven't played with NNs, but it puzzles me as to why the non-recurrent approach is so prevalent for complex tasks. I mean, it's the basic combinatorial circuit vs sequential circuits, which we all know are much more suited for complex or large outputs. Where's everything we learned from synchronous logic synthesis?

[reply to darkmighty comment below, thread depth limitation]

Indeed this is exactly it, evolution is a global method, learning is local.

I am reading John Gomez's thesis where he compares and combines learning and evolution

http://www.cs.utexas.edu/users/nn/downloads/papers/gomez.phd...

Otoro's post on the evolution of his slime volleyball thinking is well worth a read.

http://blog.otoro.net/2015/01/27/neuroevolution-algorithms/

I will check those out, thanks. This is indeed a fascinating topic. I guess every scientist wants to understand learning.

The connection comes up in david mckay's Information Theory book too, a reading I definitively recommend, although I haven't been through it properly myself.

http://www.inference.phy.cam.ac.uk/itprnn/book.pdf

McKays Information Theory is a brilliant read so far, many thanks.

Having learning couched in Information Theory terms brings it all right back to Claude Shannon's early work on Reinforcement Learning Chess programs and Alan Turing's ideas about evolving efficient machine code by bitmask genetic recombination.

Grave's Handwriting Net was trained using Backpropagation - whereby the error between the net's estimate and a training target is sent backward through the net - so the net's estimates gradually become closer to the targets.

Backpropagation takes longer the deeper the net - Recurrent Neural Nets are deep in time so Back Propagation can become intractable or unstable.

Otoro's Slimeball demo evolves a Recurrent Net rather than training it - this appears to be a very efficient method, less likely to get stuck in local minima.

The slimes evolve through self-play which is a trial and error method and reinforcement methods seem to do better on control tasks than passive learning.

Ah I see. But as far as training goes the difference between the two methods ("evolution" and backprop) is a matter of locality, no? The backprop modifies weights loosely based on local gradiet towards fitness, and evolution goes in sparse random directions. In this view backprop is indeed vulnerable to local maxima if your optimization method isn't very good, but isn't it just a matter of choosing good optimization methods? In other words, combining local backprop optimization with global evolutionary methods should be the role of robust optimization algos, no?

That handwriting demo is awesome! Thanks for the pointers - I want learn more about how that works.

I wish I stayed at U of T for a few more years...

Reply on news.ycombinator.com