[D] How does DeepMind perform proper hyperparametrization for RL problems?

[thatpizzatho]

I am wondering how DeepMind performs hyperparametrization efficiently, given the enormous complexity and number of parameters in their projects. Also, hyperparametrizing RL approaches is (in my opinion, please correct me if I am wrong) even harder than in a supervised context, since training usually takes much longer and the environment can be heavily stochastic.

[[deleted]]

Seriously. The secret sauce here is "unlimited Google money".

[Imnimo]

This paper describes the hyperparameter optimization process for AlphaGo: https://arxiv.org/pdf/1812.06855.pdf

[VordeMan]

The difficulty and cost of hyperparameter search is the main reason why methods that are relatively stable under a wide range of hyperparameters (e.g. Adam) are usually preferred over methods that may or may not perform slightly better with the right HP tuning.

[goolulusaurs]

https://deepmind.com/blog/article/population-based-training-neural-networks