Deep Reinforcement Learning
Stable Baselines rely on TF 1.x but Stable Baselines v3 rely on PyTorch.
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Stable Baselines rely on TF 1.x but Stable Baselines v3 rely on PyTorch.
In December 2016 we presented it at ENS Paris-Saclay with Étienne Simon but apparently I never wrote a blog post in the end.
Application: Given an ordered list of keys with frequencies, build a binary search tree on those keys which minimizes the average query cost.
Compute the pareto set of a given set of points in 2 or 3 dimensions.
You are given two polynomials $P$ and $Q$ and want to compute their product. The polynomials are given in form of an array with their coefficients.
You are given an array $X$ with the promise that each of its values appears exactly twice. You want to transform $X$ such that at the end all pairs are adjacent in the array. An allowed operation consists in removing a value from $X$ and appending it at the end. The cost of a solution is the maximal value which was moved.
Given an array $x$ and an integer $k$, determine for every index $1 \leq j\leq n$ the maximum $x[i]$ among all indices $\max\{1,j-k+1 \} \leq i \leq j$.
Maintain a set, allowing to add or remove elements and to query the sum of the up to k largest items.