Tutorials
We welcome more tutorials, send us any tutorials you would like to be listed to support@hexagon-ml.com and you are ok to show your name as author on it.
Date Created: June 12, 2019
KDD Cup | Humanities Track Tutorial Bandit Randomized Probability Matching
State$S \in \{1,2,3,4,5\}$ Action$A_S = [a_{ITN},a_{IRS}]$ where $a_{ITN} \in [0,1]$ and $a_{IRS} \in [0,1]$ Reward$R_{\pi} \in (- \infty,\infty)$ In [1]: import numpy as np from collections import defaultdict import random # !pip3 install git+https://github.com/slremy/netsapi --user --upgrade from netsapi.challenge import * Bandit Randomized ...
Read moreDate Created: June 4, 2019
Date Created: May 20, 2019
KDD Cup | Humanities Track Tutorial Genetic Algorithm
KDD Cup|Humanities Track Tutorial Genetic AlgorithmThis Tutorial builds on the previous tutorial to demonstrate a baseline implementation of an evolutionary search for high performing PoliciesReward$R_\pi \in (-\infty, \infty)$ Policy($\pi$) for this challenge consists of a temporal sequence of Actions. In [ ]: !pip3 install git+https://github.com/slremy/netsapi --user --upgrade from netsapi.challenge import * import ...
Read moreDate Created: May 20, 2019
KDD Cup | Humanities Track Tutorial Policy Gradients
KDD Cup|Humanities Track Tutorial Policy GradientsVanilla Policy Gradient AgentThis tutorial contains a simple example of how to build a policy-gradient based agent build on top of this Medium post. This implementation has used the Challenge Environment. In [ ]: import tensorflow as tf import tensorflow.contrib.slim as slim import numpy as np import os from sys import exit, exc_info ...
Read moreDate Created: May 20, 2019
Date Created: May 20, 2019
KDD Cup|Humanities Track Tutorial Q-Learning
KDD Cup|Humanities Track Tutorial Q-LearningThis Tutorial builds on the previous tutorial to demonstrate a baseline implementation of a standard Reinforcement Learning (RL) Algorithm¶State$S \in \{1,2,3,4,5\}$ Action$A_S = [a_{\mathrm{ITN}}, a_{\mathrm{IRS}}]$ where $a_{\mathrm{ITN}} \in [0,1]$ and $a_{\mathrm{IRS}}\in [0,1]$ Reward$R_\pi \in (-\infty, \infty ...
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