These are all from the sub-field of reinforcement learning (RL). The first 2 and the last are evolutionary methods, and from the abstract the third is some flavor of model-based RL.

Any course on reinforcement learning will give you a decent background in understanding the basics of how these classes of algorithms work.

Here's a link to a curated set of resources for beginners/intermediates in RL: https://github.com/andyljones/reinforcement-learning-discord-wiki/wiki

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I think it'd important to understand the background so you aren't lost when you're trying to apply these on problems, but that said, I won't recommend trying to implement these from scratch yourself: start from open-source baselines instead; there's a lot of tiny details to these algorithms that are hard to test, and one thing going wrong can make the whole algo fail in ways that are really hard to debug.