In reinforcement learning which is used for things like AlphaGo, there is a similar duality called "exploration vs exploitation": - do you keep searching in br

In reinforcement learning which is used for things like AlphaGo, there is a similar duality called "exploration vs exploitation": - do you keep searching in breadth for new avenues - or is it time to dive in depth in what you found so far. And this is even optimized through something called "regret". - example: https://rlss.inria.fr/files/2019/07/exploration_lazaric_1.pdf https://rlss.inria.fr/files/2019/07/exploration_lazaric_1.pd...