RNN inference on a smaller edge controller (all history is cached in a single state point for each layer, so much less memory and computation requirements IIRC) :')
Very mobile-device and battery-powered systems friendly. :')))) ;'DDDD
My understanding is that a 1BN transformer is about 2BN flops/inference, so about 1TFLOP for a 500 sequence of inferences (and also about several GB of memory)
What would be the equivalent RWKV (let ignore the inevitable loss penalty which could be significant..)
(there's a discord, you should join it with further questions! I unfortunately am not as informed as I should be on this one, other than the fact that it is _very_ mobile friendly). The performance diff is slight but not too bad really, all things considered. And I think it comes out on top for raw efficiency per parameter/flop, IIRC.
Sigh. Do discussions about RWKV always end with suggestions that I join the Discord? If I do join the Discord, will I soon begin suggesting that others join the Discord as well? What I mean is, I've seen this come up a few times on HN and discussions usually end prematurely with suggestions to join the Discord. [0]
If this technique is good, I'll wait until I can learn about it without joining the Discord.
Cool to see an approach to using transformers that sticks closer to traditional RL than the decision transformer. The action dimension trick here is clever. Curious to see where this can be taken, game playing, multiagent, etc.
+1 you beat me to the punch! I think its helpful to start with simple RL and ignore the "deep" part to get the basics. The first several lectures in this series do that well. It helped me build a simple "cat and mouse" RL simulation https://github.com/gtoubassi/SimpleReinforcementLearning and ultimately a reproduction of the DQN atari game playing agent: https://github.com/gtoubassi/dqn-atari.
Honestly the best way is starting with implementing a Q table for some small grid-world problem. You get a lot of knowledge from doing that. Then a bit more work on understanding various approaches, e.g policy learning, world models. Then, reading text books, blogs tutorials, etc.
But "getting" the idea of Q learning for a small state space is fundamental and surprisingly approachable.
I wrote this extensive tutorial for teaching deep reinforcement learning, with a focus on getting intuition from code. you will find RL theory is heavy on math despite needing math for very little other than abstractly representing some machine goal and intuition, of which code serves a native programmer already very well.
i spent years failing to learn machine learning and RL until i just started reading source code. books of integrals i never ended up needing.
dont be turned away by the joking nature of my tutorials. there is a real depth in there
I enjoyed the book Grokking Deep Reinforcement Learning from Manning. It's written in a very accessible style and explains the mathematical formulas you will see in other RL teaching material.
I'd suggest getting a good book or other teaching resource and solve a few Gymnasium[0] environments. Unlike supervised machine learning, you don't need someone else's data, you generate your own data.
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Very mobile-device and battery-powered systems friendly. :')))) ;'DDDD
Just how much compute/memory are we saving here?
My understanding is that a 1BN transformer is about 2BN flops/inference, so about 1TFLOP for a 500 sequence of inferences (and also about several GB of memory)
What would be the equivalent RWKV (let ignore the inevitable loss penalty which could be significant..)
It only requires the previous state.
(there's a discord, you should join it with further questions! I unfortunately am not as informed as I should be on this one, other than the fact that it is _very_ mobile friendly). The performance diff is slight but not too bad really, all things considered. And I think it comes out on top for raw efficiency per parameter/flop, IIRC.
An interesting concept, for sure! :'DDDD :'))))
If this technique is good, I'll wait until I can learn about it without joining the Discord.
[0]: https://news.ycombinator.com/item?id=35508692
But "getting" the idea of Q learning for a small state space is fundamental and surprisingly approachable.
I wrote this extensive tutorial for teaching deep reinforcement learning, with a focus on getting intuition from code. you will find RL theory is heavy on math despite needing math for very little other than abstractly representing some machine goal and intuition, of which code serves a native programmer already very well.
i spent years failing to learn machine learning and RL until i just started reading source code. books of integrals i never ended up needing.
dont be turned away by the joking nature of my tutorials. there is a real depth in there
I'd suggest getting a good book or other teaching resource and solve a few Gymnasium[0] environments. Unlike supervised machine learning, you don't need someone else's data, you generate your own data.
[0]: https://gymnasium.farama.org/