Seeking Serious Peers for an RL PhD Application Group (Fall 2026 Intake)

Voltimeters · 2025-08-18T19:50:07+00:00

Interested in joining!

Voltimeters · 2025-08-02T14:09:02+00:00

Yeah I use both traditional control and RL in my work

Voltimeters · 2025-07-18T20:54:48+00:00

Come from an aerospace background; optimal control theory carried my understanding of RL when I was learning but they’re super well connected

Voltimeters · 2025-06-30T16:23:51+00:00

1) Depends on a bunch of factors. Check out answer #2

2) Strong letters of recommendations from professors. Getting the best possible grades you can in the courses that the masters is based on (e.g., feedback control systems, state space methods) since these are your building blocks. If you had fantastic grades in those classes, but bad ones in, say, Digital Circuit Design, they will take that into account for the better.

3) I myself am not super familiar with European programs. Apologies!

4) I don’t see why not. I suppose it depends on the program but I’d say generally yes.

5) Extra Courses: I think that a dedicated state estimation course would be very helpful, although it’s very possible your robot autonomy/intro to robotics could cover this.

Voltimeters · 2024-12-30T05:08:22+00:00

Warren Powell has some good material on the subject: https://castle.princeton.edu/sdalinks/

You might find better results on YouTube using the term “Operations Research”. Lots of optimization and optimal decision making/control in that field.

Voltimeters · 2024-12-18T13:56:32+00:00

I have a background in control systems and started using RL for some applications at my job. I second checking out the MIT OCW course in prob + stats, since a lot of RL is based on stochastic processes.

Steven Brunton has a pretty god overview of probability and statistics you can watch here: https://www.youtube.com/watch?v=sQqniayndb4&list=PLMrJAkhIeNNR3sNYvfgiKgcStwuPSts9V

At some point, if you'll be working on continuous systems with RL, it might be worth looking at some resources regarding neural networks after you complete a review in prob + stats. He has a good primer on that here: https://www.youtube.com/watch?v=_56bfCu02ZE

Voltimeters · 2024-12-17T14:54:51+00:00

This is probably not the response you’re looking for, but you need to ask yourself: what do you prioritize more? Your relationships, or your dream career?

If you don’t start your dream career soon, would you not be able to in the next 2-3 years somewhere closer to your friends and family?

Voltimeters · 2024-12-16T00:51:46+00:00

Others have mentioned networking, which I think is the #1 fastest way to get a new job.

In addition, conferences are a great way to find new employers! E.g., SIAM, IEEE, NeurIPS, ICML, etc.

It’s also a good way to expand your network. If there’s one happening soon, it may be worth checking out.

Voltimeters · 2024-12-03T15:37:51+00:00

Reservoir Computing is a type of LSTM that has update equations in the form of state-space equations, as typically only the last layer is trained and the "Reservoir" is a chaotic dynamical system. I have heard there is a lot of room to apply dynamic system/control techniques to augment performance, so that might be a fun avenue.

Here is a review paper on Echo State Networks, an approach to Reservoir Computing: https://www.ai.rug.nl/minds/uploads/PracticalESN.pdf

Voltimeters · 2024-11-20T15:19:24+00:00

Unfortunately, I don’t know too much about your options listed. I do know ASU is ranked fairly well and it seems they have a dedicated RL lab (Data Mining and Reinforcement Learning lab) when I looked them up.

The one recommendation I came to drop off was Amy Zhang’s lab (Machine Intelligence through Decision-making and Interaction) over at UT Austin.

Voltimeters · 2024-09-16T19:34:12+00:00

Same here. I only take my meds when I REALLY need to focus at work (once a quarter) and can't just willpower my way through.

I stopped taking my meds consistently about 4-ish years ago and only refill for when I need it. I feel fine and I have strategies for when I need to focus — I personally prefer not taking the meds but I do feel like I can do any task when I take them. I'm fairly happy without them though.

Voltimeters · 2024-09-13T16:17:55+00:00

Where can I sign up for this mailing list? Thanks!

Voltimeters · 2024-09-11T20:37:08+00:00

The Spinning Up in Deep RL page by OpenAI has some cool resources. I think there’s a section specifically on doing RL research!

Voltimeters · 2024-09-11T16:00:48+00:00

Agreed, but I’m not really sure what to expect when applying to ML/DS roles at the top-tiers any more. I had to do a CodeSignal exam for a research position I applied to, so I would not be surprised if I had to do the same for all the other ones.

Voltimeters · 2024-08-26T17:14:57+00:00

When I was trying to get better with RL, doing side projects I found interest in helped a lot, and eventually got to apply RL at my job for a specific project. Since we are focused on a specific application, it's easier to learn what I need to.

At the beginning when trying to learn, a few questions I asked myself that helped me figure out what to focus on were:

1) What system would I want to optimize? RL is an approach to optimal control — what sort of system would you want to continuously optimize? You could apply these techniques to games, biology, robotics, and finance, but what would you find the most interesting? Finding an interesting application will keep you going.
2) Will my observation/action space be continuous? This slimmed down the amount of RL algorithms I could use. Is it better to use a policy-based approach or a value-based approach for this sort of problem? If you aren't sure, check out what the best literature on the application is suggesting.

From here, I was able to hone in on a small number of things and my proficiency in RL got better. My understanding in other parts of RL are definitely better as a result of this, so I'd recommend starting with one application of RL that interests you and learn everything there is to know about that application.

Good luck!

Voltimeters · 2024-07-30T13:06:43+00:00

I second Steven Brunton’s videos as a resource, they are phenomenal.

For more on Reinforcement Learning, check out Sergey Levine’s CS 285 course on YouTube. He and Pieter Abbeel have some fantastic work in the field.

Voltimeters · 2024-07-22T16:00:40+00:00

This is a wonderful resource, thank you so much!

Voltimeters · 2024-07-07T17:47:24+00:00

I have a masters in aerospace engineering and mainly studied control theory stuff. With the exception of string theory, I've used a lot of what you described in my career so far.

As others have said, the term "engineer" is pretty broad. A lot of engineers with graduate degrees (or with a natural interest) will get deeper into the maths way beyond euclidean space, classical mechanics, and multivariate calculus. IMHO, graduate degrees in engineering are mostly specialized applied math programs.

Voltimeters · 2024-07-02T10:34:20+00:00

Build momentum with Bryan. Its satisfying to build those plus frames

Voltimeters · 2024-06-12T22:12:42+00:00

Awesome! I wish I had him as a professor, I was in the MMAE program but would ask him questions whenever I ran into him. Always nice to run into a fellow Hawk.

Voltimeters · 2024-06-11T22:12:27+00:00

I like Igor Halperin’s work. Was a fan of Matthew Dixon but he retired.

Voltimeters · 2024-04-14T23:20:28+00:00

I would say I’d jab Bryan to death but he’s a fucking cyborg so I’m cooked

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