Neuromorphic computing implementation of pole balancing(neuromorphic.eecs.utk.edu)
neuromorphic.eecs.utk.edu
Neuromorphic computing implementation of pole balancing
http://neuromorphic.eecs.utk.edu/pages/research-demos/
8 comments
Two-pole balancing (2009):
https://www.youtube.com/watch?v=fqk2Ve0C8Qs
Double inverted pendulum balancing (2015), a much harder task:
https://www.youtube.com/watch?v=8t3i2WPpIDY
Double inverted pendulum balancing with a physical cart (2011), a much much harder task:
https://www.youtube.com/watch?v=B6vr1x6KDaY
Triple!!! inverted pendulum balancing with a physical cart (2011), a much much much harder task:
https://www.youtube.com/watch?v=cyN-CRNrb3E
https://www.youtube.com/watch?v=fqk2Ve0C8Qs
Double inverted pendulum balancing (2015), a much harder task:
https://www.youtube.com/watch?v=8t3i2WPpIDY
Double inverted pendulum balancing with a physical cart (2011), a much much harder task:
https://www.youtube.com/watch?v=B6vr1x6KDaY
Triple!!! inverted pendulum balancing with a physical cart (2011), a much much much harder task:
https://www.youtube.com/watch?v=cyN-CRNrb3E
The single inverted pendulum balancing problem has been solved using neural nets, fuzzy logic, and nonlinear control theory. It's a standard problem in controls classes.
Here's a system learning how to do this.[1] Takes about 200 trials.
Here's the triple inverted pendulum balancing problem, solved using feedforward control.[2]
[1] https://www.youtube.com/watch?v=Lt-KLtkDlh8 [2] https://www.youtube.com/watch?v=cyN-CRNrb3E
Here's a system learning how to do this.[1] Takes about 200 trials.
Here's the triple inverted pendulum balancing problem, solved using feedforward control.[2]
[1] https://www.youtube.com/watch?v=Lt-KLtkDlh8 [2] https://www.youtube.com/watch?v=cyN-CRNrb3E
Are there any non-evolutionary training methods for neuromorphic networks?
Currently, fixed architecture ANNs can solve the cartpole problem very quickly already with Q-learning or policy gradient methods: https://gym.openai.com/envs/CartPole-v0
It seems like some kind of neuromorphic networks are going to be necessary for the long term AI 'dream', but there really needs to be something better then evolutionary algorithms for training, those just don't scale.
Currently, fixed architecture ANNs can solve the cartpole problem very quickly already with Q-learning or policy gradient methods: https://gym.openai.com/envs/CartPole-v0
It seems like some kind of neuromorphic networks are going to be necessary for the long term AI 'dream', but there really needs to be something better then evolutionary algorithms for training, those just don't scale.
Learning algorithms remain a big issue in neuromorphic computing. So far, we have had some success with evolutionary optimization algorithms. The nice aspect of EO at the this stage of the game is that EO can reveal useful patterns and network features which could guide later learning systems.
I am working on a project right now which will require a more complex network interacting with many real world sensors, so it should be interesting to see how EO performs for such a problem. Our EO has been demonstrated to scale on parallel machines as large as ORNL's Titan, but I readily acknowledge that much more work needs to be done with learning algorithms.
I am working on a project right now which will require a more complex network interacting with many real world sensors, so it should be interesting to see how EO performs for such a problem. Our EO has been demonstrated to scale on parallel machines as large as ORNL's Titan, but I readily acknowledge that much more work needs to be done with learning algorithms.
http://www.scholarpedia.org/article/Spike-timing_dependent_p...
But it kind of requires the structure of the cortical columns to already be in place - which has happened through genetics/evolution in biology.
Biologically plausible backpropagation could also work, but I haven't seen anyone successfully apply it in SNN's
But it kind of requires the structure of the cortical columns to already be in place - which has happened through genetics/evolution in biology.
Biologically plausible backpropagation could also work, but I haven't seen anyone successfully apply it in SNN's
"A Deep Learning system does not have a temporal component" but isnt that what recursive deep learning is for.
And how do they calibrate low/middle/high.
And how do they calibrate low/middle/high.
> "A Deep Learning system does not have a temporal component" but isnt that what recursive deep learning is for.
Yes, recurrent nets exist, and have since the 80s, before even convolutional nets. The authors seem a bit out of the loop, considering the (more recent) success of RNNs in speech recognition... Arguably this article itself describes a type of RNN.
Yes, recurrent nets exist, and have since the 80s, before even convolutional nets. The authors seem a bit out of the loop, considering the (more recent) success of RNNs in speech recognition... Arguably this article itself describes a type of RNN.
Roughly speaking, low/middle/high are just buckets, each of which represents a third of the distribution for position, velocity, or angle.
We attempt to illustrate that in this image: http://neuromorphic.eecs.utk.edu/images/graphics/Inv-Example...
We attempt to illustrate that in this image: http://neuromorphic.eecs.utk.edu/images/graphics/Inv-Example...
Ok so this looks like Fuzzy Logic? Or does the system automatically determine the low/middle/high boundaries?
I understand that some of the inputs don't affect the behavior and so don't connect to anything, but why do some of the synapses not connect to anything? In particular, what is the meaning of the two nodes above and below the outputs. They are on long chains that appear meaningless. Why did the network grow there; is it just evolutionary noise?
Unless I'm looking at it wrong, the synapse above the output takes input from 2 up and 2 to the left, which is a path that leads back to an input.
But you're right that the synapse right below the outputs has no input, which doesn't make any sense. The original generated network had a neuron with no inputs at (6,12) just to the left of the outputs, left over from the evolutionary algorithm like you suggested. We ran a program that was supposed to prune the network by removing the evolutionary noise that had no effect on the outputs. It looks like that program just removed any neurons with no inputs, leaving behind that meaningless path of synapses.
But you're right that the synapse right below the outputs has no input, which doesn't make any sense. The original generated network had a neuron with no inputs at (6,12) just to the left of the outputs, left over from the evolutionary algorithm like you suggested. We ran a program that was supposed to prune the network by removing the evolutionary noise that had no effect on the outputs. It looks like that program just removed any neurons with no inputs, leaving behind that meaningless path of synapses.
The evolutionary process does not automatically prune elements. It is possible that previous non-useful features of a network become helpful at a later stage of evolution.
How was the wiring between neurons and synapses created?
There's a brief description here http://neuromorphic.eecs.utk.edu/pages/research-overview/#pr....
Basically, the inputs and outputs are fixed, random neurons and connections are added, the networks are all tested, the better ones are changed and/or combined, and then repeat until a "good" network is found, where good in this case means balancing the pole for a long time without running into the walls.
Basically, the inputs and outputs are fixed, random neurons and connections are added, the networks are all tested, the better ones are changed and/or combined, and then repeat until a "good" network is found, where good in this case means balancing the pole for a long time without running into the walls.
Interesting. This could map onto a cellular automata. Do the connections evolve from left to right? Or do you start with a static size universe?
The grid size and the location of the inputs and outputs are fixed throughout the process. Are you suggesting the connections could grow left to right as it evolves? I think that'd be interesting, but I'm not sure if it would be useful.
But here, from the claims in the post and the lab website, it sounds as if the goal is in application: Creating better, more efficient controllers. This comes across as a little detached from the applied machine learning literature. At the least, I missed a comparison to reinforcement learning (which has a history of learning to solve this exact task with simpler controller designs and most likely shorter search times) and also to non-bio-inspired recurrent networks.
One more point: Even if I follow along with the claim that 'deep learning' approaches don't have memory (implying recurrent networks aren't included in that label), I want to point out that this particular task setup, with positions/angles as well as their rates of change provided, can be solved by a memoryless controller. It would have done more to highlight the strengths of the recurrent network approach if a partially observable benchmark task had been used, e.g. feeding positions and angles only. Much more difficult high-dimensional tasks e.g. in robotic control are tackled in the (deep) reinforcement learning literature among others.