adversarial-robustness-toolbox VS alpha-zero-boosted

Check out this project https://github.com/Trusted-AI/adversarial-robustness-toolbox

Adversarial Robustness Toolbox (ART) is a Python library for Machine Learning Security. ART provides tools that enable developers and researchers to defend and evaluate Machine Learning models and applications against the adversarial threats of Evasion, Poisoning, Extraction, and Inference.

Depending on what attacks you want I've found both https://github.com/cleverhans-lab/cleverhans and https://github.com/Trusted-AI/adversarial-robustness-toolbox to be useful.

> I came up with a nifty implementation in Python that outperforms the naive impl by 30x, allowing a pure python MCTS/NN interop implementation. See https://www.moderndescartes.com/essays/deep_dive_mcts/

Great post!

Chasing pointers in the MCTS tree is definitely a slow approach. Although typically there are < 900 "considerations" per move for alphazero. I've found getting value/policy predictions from a neural network (or GBDT[1]) for the node expansions during those considerations is at least an order of magnitude slower than the MCTS tree-hopping logic.

[1] https://github.com/cgreer/alpha-zero-boosted

What you can do is checkout the algorithm at a particular stages of development. AlphaZero&Friends start out not being very good at the game, then over time they learn and become super human. You typically checkpoint the weights for the model at various stages. So early on, the algo would be like a 600 elo player for chess and then eventually get to superhuman elo levels. So if you wanted to train you can gradually play against versions of the algo until you can beat them by loading up the weights at various difficulty stages.

I implemented AlphaZero (but not Mu yet) using GBDTs instead of NNs here if you're curious about how it would work: https://github.com/cgreer/alpha-zero-boosted. Instead of saving the "weights" for a GBDT, you save the splitpoints for the value/policy models, but the concept is the same.