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The TF-Agents team replicated the RL training (with the corresponding teams' very deep collaboration) and open-sourced it here: https://github.com/google-resea
by yablak 4y ago
The TF-Agents team replicated the RL training (with the corresponding teams' very deep collaboration) and open-sourced it here:
https://github.com/google-research/circuit_training https://github.com/google-research/circuit_training
It pretty much gets the same results as found in the Nature paper.
The original codebase was heavily research-focused, used TF1, was impossible to run distributed training outside of Google's infra, and made it hard to try algorithms other than PPO. So it was reimplemented on top of TF2 and using some distributed training and collection technologies developed by the TF-Agents team at Google Brain and infra teams at DeepMind.
Everyone is welcome to poke at the training code and the model, and convince themselves that it does what it says on the box :)
- deleted 4y ago[deleted]
- Throw4w5y 4y ago> the RL training Is RL necessary in this context or would other, simpler methods work as well to within statistical error? RL can be a heck of a random number generator if misapplied.
- ensan 4y agoThe method proposed in their Nature method may be very impactful, but the paper itself is nowhere near as rigorous as the other Nature papers I've read; it is written more like a white paper and I believe if this paper was submitted by a university research group (even in its final form as we're seeing right now), it would have been rejected. So I do sympathize with the person raising concerns about it.
- sijnapq 4y agoIf you seek truth, search on moma or ask for experiments for nature rebuttal document. Read the document and come back for comments :)