by Martin Haring -

Reinforcement Learning – The mountain car game, Part 2

I did some experiments with this small example game. My goal was to find effective AI settings; settings that would always lead to quick success. Last time, the AI did manage to beat the game after ~180 attempts – these settings were the starting point of a long testing session.

In this blog, I will go over the results of the experiments.

by Martin Haring -

Reinforcement Learning – The mountain car game, Part 1

I followed a TensorFlow tutorial that puts the paradigms mentioned some blogs prior (Q-Learning, epsilon-greedy policies) to practice. I implemented an AI that learns to play the ‚mountain car game‘, which is about getting a small car onto the peak of a mountain. This is done by swinging left and right, gaining momentum and using that to win. To increase the difficulty a bit, there is a time constraint of three seconds.

This blog is about the setup and the inital results.

by Martin Haring -

Reinforcement Learning – Starting to getting started

So far in this project no programming was needed. For the next step, which is to take a closer look on TensorFlow, a simple Notepad will not be sufficient. That is why this blog is about one of the first thing to do when it comes down to the development: choosing the right Integrated Development Environment (aka IDE), installing the best choice and getting TensorFlow running.

To be precise, this blog is about possible IDE options and  what to keep in mind when installing TensorFlow.

by Martin Haring -

Reinforcement Learning – Advanced practices

Last week I gave a quick introduction to Q learning. This week I want to follow up to this topic by taking a closer look on more advanced development practices than those used in MarI/O.

This blog covers three useful practices in the field of reinforcement learning. The sources mentioned at the end provide code examples that will be useful for future experiments.

by Martin Haring -

Reinforcement Learning – Introduction to Q-Learning

So far, the AI learned to play a game by interacting with its environment and maximizing a desired reward. Practically, the AI just repeatedly played the game; getting better with each iteration. To be successful, it is necessary to act upon a policy. However, there are other approaches for this case: the so-called “Deep Q network” or the “epsilon-greedy policy”. I will focus on the former for one main reason: it is compatible with TensorFlow, which is a python library I wanted to take a closer look on anyway.

This blog serves as an introduction into the paradigm of Q-Learning.

Gamification of Elections - Part 6
by Sebastian Benjamin Reichl -

Cast Vote? Yes / No / Cancel

GAMIFICATION OF ELECTIONS – PART 7

In this final article I will circle back to the original question: What to do about declining voter turnout. How to reinvigorate the interest in voting – especially for a new generation, trained on instant gratification and stuck in constant feedback loops. Working on this from the angle of a game designer won’t deliver a cure-all solution. It probably won’t even make a dent in the graphs. But the general idea is a powerful one, and if just a handful of people can be attracted to politics, into electoral studies or more generally in the process of democracy, then the effort should be worth it.