Intro In this post we report success in using reinforcement to learn the game of nim. We had previously cited two theses (ERIK JÄRLEBERG (2011) and PAUL GRAHAM & WILLIAM LORD (2015)) that used Q-learning to learn the game of nim. However, in this setting, the scaling issues with Q-learning are much more …
There are entire theses devoted to reinforcement learning of the game of nim, in particular those of ERIK JÄRLEBERG (2011) and PAUL GRAHAM & WILLIAM LORD (2015). Those two were successful in training a reinforcement-based agent to play the game of nim with a high percentage of accurate moves. However, they used lookup tables …
This post reports on the creation of a python package for the game of nim. The package contains a function that finds the perfect move in a given position and informs on whether the position is winning. In the package we make use of Charles Leonard Bouton’s solution of the …
So now we’re ready to deploy our own custom R Shiny server with caching. We had previously already discussed the pros and cons of hosting your own server, by this we mean a docker based server in the cloud Signing up at https://www.shinyapps.io/ See this https://arthought.com/r-shiny-stock-analysis. Then we had opted …
In the previous post we looked at a simple data caching example which we used to explore the workings of the R-package DataCache. In this post we continue with this exploration. Instead of just using system time as the datafeed we now use a more real world example of financial …
This is in preparation for running a custom Shiny server. We want to accelerate the server by using caching. In the this post we take a look at a candidate caching package. In this post we’ll explore a the package DataCache. It is a very useful package, however, I found that …
This post starts a mini series of two posts in which we want to solve the Game of Nim using Reinforcement learning. The first part of this mini series is devoted to having a look at using our own Nim-specific custom environment for OpenAI. The Game of Nim is a …
Introduction This post we deploy our own R-shiny app. You can select a stock The app fetches the data, does some number crunching and plots the results The source code is on github.
This post we turn to R-shiny. First, we embed someone else’s existing shiny-app as an example to check that everything works as expected. For the embedding we use an iframe. We install the wordpress plugin Reframer The R-shiny app is from http://stla.github.io and is referenced in this post
Recap: Previously we constructed a very simple class to emulate the type of environment which is provided by OpenAI. Windy Walk (part 1) Then we implemented the same windy walk model as an extension to OpenAI. For this we created a custom python package, we named it gym_drifty_walk, you can grab it …