If you want to go for a PhD, I suggest reading Sutton's book on RL (at least the important parts for what you want to do), followed by recent RL papers and RL papers in robotics that pick your interest, understand them fully. Aim for journal papers and major conferences only (high impact factor is a first metric that comes to mind).
The main bit is to try to formulate an important research question you're interested in which is not already explored in the vast litterature and plan which experiments you need to carry and what do you need to change if some of them fail.
If you want to engage in a PhD, an important and well studied research question will increase your chances of getting accepted in a PhD program.
The main advantage in doing a PhD in a uni is that you can share and learn knowledge from other students and advisors, as well as have insights in what are ideas worth pursuing and what are small improvement that no one care about.
At uni you usually work on the kind of research meant to improve the knowledge and less on the general engineering problems. Engineering problems are byproduct of the knowledge you are building. A lot of uni work very closely with industries in practical fields like robotics, you'll have plenty of opportunities to apply what you are building directly on engineering problems. But that will not be the main focus.
In my experience it is considered less 'valuable research' to try to apply some SoTA to a specific engineering problem in academy, and usually proposals of this kind are rejected.
I've read part of the book, and followed David Silvers UCL lectures on my own, and have been focused on reading all the recent algorithm/approaches in the field last few weeks.
>RL is extensively used in robotics ;)
Is it? From Andrej Karpathy:s HN comment[1] and Alex Ipran's[2] comment in 2017 & 2018 I feel like it is just recently (around mid 2019) actual trial on robotics have started to show promise (SAC, E2E) in academia, and only a handful of start ups have approached this (Covariant, founded by one of the professor in the two papers above) But then again, I might be missing some insight here.
Thank you for the extremely valuable feedback! :) The formulation of important research question is also something I'm trying to figure out by, getting myself updated in the field to the point of understanding what is missing (or under-studied)!
As I mentioned in another comment I'm in the process of applying at a public research institute (as an research assistant) with potential for an industrial PhD position, so those feedback is just on-point valuable for me right now :)
(Although the research is not RL focused but is trying to tackle the identical problem formulation in robotics using ML/AI algorithms)
DL/RL/etc. Are very hype right now in research, everything is moving at a very fast speed. A lot of 'RL applied to robotics' papers are accepted at top conferences (and even more rejected)
Several tools are in development to increase dramatically the speed at which research can be done ;)
A simple Google scholar search on reinforcement learning and robotics limited to 2020 only should give you lots of good results.
If you want to go for a PhD, I suggest reading Sutton's book on RL (at least the important parts for what you want to do), followed by recent RL papers and RL papers in robotics that pick your interest, understand them fully. Aim for journal papers and major conferences only (high impact factor is a first metric that comes to mind).
The main bit is to try to formulate an important research question you're interested in which is not already explored in the vast litterature and plan which experiments you need to carry and what do you need to change if some of them fail.
If you want to engage in a PhD, an important and well studied research question will increase your chances of getting accepted in a PhD program.
The main advantage in doing a PhD in a uni is that you can share and learn knowledge from other students and advisors, as well as have insights in what are ideas worth pursuing and what are small improvement that no one care about.
At uni you usually work on the kind of research meant to improve the knowledge and less on the general engineering problems. Engineering problems are byproduct of the knowledge you are building. A lot of uni work very closely with industries in practical fields like robotics, you'll have plenty of opportunities to apply what you are building directly on engineering problems. But that will not be the main focus.
In my experience it is considered less 'valuable research' to try to apply some SoTA to a specific engineering problem in academy, and usually proposals of this kind are rejected.
Hope this is helpful :)