This is the last article of a three-part series on using deep reinforcement learning (RL) to teach an AI to play Doom. Over the course of the project, we saw how to apply state-of-the-art RL algorithms to solve a fun challenge like playing a first-person shooter. The emphasis was put on how to use existing tools in practice rather than on rewriting the latest algorithms.

The first part focused on establishing a basic setup that allows interacting with a Doom game instance. Thanks to a library called “ViZDoom”, we could unfold the game step by step, collecting game variables like…

This is the second part of a series dedicated to practicing reinforcement learning (RL) by teaching an agent to play various Doom scenarios. In the first part, which you can find here, we focused on presenting the setup and we solved our first RL task by training an agent to move and shoot in a simple environment. There is a Jupyter notebook which covers in details the concepts seen here.

We will build upon the setup introduced in the first part and gradually start solving more difficult scenarios. …

Reinforcement learning (RL) is a hot topic right now thanks to self-driving cars and (super)human-like performance in games like Go or Dota. However, approaching the subject can be intimidating. I had been wanting to give it a shot for quite some time but didn’t really know where to start. After a couple of initial unfruitful attempts, I started getting some gratifying results, having fun and learning a lot in the process. With the right tools and resources, it’s actually a lot easier than it looks. …

Leandro Kieliger

Data Scientist at Swisscom, transforming network interactions into actionable insights.

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