It seems like there might be a trend to use evolutionary learning (genetic algorithms) in conjunction with RL. That is what Deepmind did with the recent open-e

It seems like there might be a trend to use evolutionary learning (genetic algorithms) in conjunction with RL. That is what Deepmind did with the recent open-ended play agents [1] in order to create more generally capable agents (among a lot of other specific things). In the DeepMind case the largest variable is the environment. In the Intel case the largest variable is the the humanoid component. However both are using task specificity in some narrow task to then bootstrap a combination function to solve more general tasks. That's definitely a good approach and definitely has a lot of good history showing it's effectiveness. The reason it's notable is because of how it's acknowledging that ensemble methods really are powerful and (especially with RL) that those are likely the way toward extremely general and flexible agents. [1]https://deepmind.com/blog/article/generally-capable-agents-emerge-from-open-ended-play https://deepmind.com/blog/article/generally-capable-agents-e...