vortex-auv VS circuit_training

https://github.com/vortexntnu/vortex-auv (both these are tethered but if you want untethered you can find them in the commercial defence area easily)

u/Khay_ That would be awesome, you are very much welcome to contribute :) I have a lot of thoughts and ideas for the project, but haven't mapped it out yet. What if I tried to set up a backlog for the project as chronological steps and some overall goals, and you can have a look? I have done something similar in the past, but only for an autonomous underwater vehicle written in C++ and Python using ROS (have a look here), and learned a lot from that. I would like to create something similar, but I know a lot of things that can improve from the past architecture.

>> It is sad that you are providing a platform for someone's resentments.

The claims about independent replication refer to Google's circuit_training repository[1]. The UCSD team has conclusively shown this claim was materially false (see section 3 of their paper[2]).

BTW, Prof. Andrew Khang, who headed the UCSD effort, initially wrote an exteremely favorable editorial about the Nature paper[3].

[1] https://github.com/google-research/circuit_training

[2] https://arxiv.org/pdf/2302.11014.pdf

[3] https://www.nature.com/articles/d41586-021-01515-9

Depending on your current level and coding knowledge I would highly recommend to build on existing RL-platform such as e.g. Circuit-Training, and then potentially explore RL-aspects orthogonal to the original paper in your work. Examples could be adopting some of the recent work on more effective sample spaces, quantifying uncertainties in the design process with regards to the optimality of the design, or adding more a further degree of freedom to the framework.

On the other hand, the research is open sourced here: https://github.com/google-research/circuit_training

The TF-Agents team replicated the RL training (with the corresponding teams' very deep collaboration) and open-sourced it here:

https://github.com/google-research/circuit_training

It pretty much gets the same results as found in the Nature paper.

The original codebase was heavily research-focused, used TF1, was impossible to run distributed training outside of Google's infra, and made it hard to try algorithms other than PPO. So it was reimplemented on top of TF2 and using some distributed training and collection technologies developed by the TF-Agents team at Google Brain and infra teams at DeepMind.

Everyone is welcome to poke at the training code and the model, and convince themselves that it does what it says on the box :)