OpenAI's Universe utilises RL algorithms and I have heard of some game-training projects using Q learning, but are there any others which are used to master/win games? Can genetic algorithms be used to win at a game?
As I see it, it all comes down to game theory, which can be said to form the foundation of successful decision making, and is particularly useful in a context, such as computing, where all parameters can be defined. (Where it runs into trouble is with the aggregate complexity of the parameters per the combinatorial explosion, although Machine Learning has recently been validated as a method of managing intractability specifically in the context of games.)
Yes, evolutionary algorithms (EAs) can be used to solve/play games too. For example, OpenAI has used evolution strategies (a subset of EAs that uses fixed-length real-valued vectors and self-adaptive mutation rates) to play Atari games. In this blog post, they write
We've discovered that evolution strategies (ES), an optimization technique that's been known for decades, rivals the performance of standard reinforcement learning (RL) techniques on modern RL benchmarks (e.g. Atari/MuJoCo), while overcoming many of RL's inconveniences.
There is also the related paper Evolution Strategies as a Scalable Alternative to Reinforcement Learning (2017) and code.