Requests for Research
Requests For Research (RFRs) are selected projects that are expected to have a significant, valuable impact on state-of-the-art research and knowledge.
By publishing these RFRs, we can can engage other researchers to complete the work we can’t do by ourselves. At the current time, the Self-Organizing RFR below has been completed and is in writeup, and other groups have answered the 3D agent request by developing the “Animal AI Olympics“
Current RFRS
- Long-range navigation without perfect information using local reinforcement learning rules
- Using Reinforcement Learning to discover attentional strategies
- Extending autoassociative memories
- Improving episodic memory with disentangled representations
- Self-organizing neural network hierarchies - Part 2
- Decision Making
- Improving Memory with a Hippocampal Model
- 3D agent test suites
- Self-Organising Architectures
- Left and Right Neural Networks – Inspired by our Bicameral Brains
The RFRs presented here are jointly compiled by Project AGI and the Whole-Brain Architecture Initiative (WBAI). Our shared objective is to foster research into general intelligence. The RFRs are selected to encourage research that we believe will help to realise human-like Artificial General Intelligence (AGI) via the whole brain architectureapproach, which assumes that the path to AGI can be shortened by mimicking the macro-architecture of the brain.
The RFRs are substantial projects and we expect are best suited to individuals or groups with the following knowledge and skills:
- Ability to program (e.g. simulations, technical models)
- At least basic machine learning (ML) experience
- Basic knowledge on the architecture of the brain (neuroscience)

But you don’t have to worry. For learning ML, there are ample materials on the Web (see our recommendations). To get some background in neuroscience, you can start with the site here (CCNBook).
More importantly, you won’t work alone. You are encouraged to contact us before you start working on the requests. Then you’ll be part of our community where you can get support including resources (such as software and data) and advice. We can also help to link you with like-minded researchers so that you can attack these problems as a team.
If you start work on one of our RFRs, please provide a link back to our page[s] when you publish your results.
Contact
Public forum (for discussion, team-building and support): Please use reddit here:
https://www.reddit.com/r/BioAGI
Other inquiries: please send your message to this address: rfr [at] wba-initiative.org
FAQs
- If I work on the RFR, who owns the intellectual property (IP)? Am I free to publish my work?
- You (and your team) own your work and full rights to your IP. We publish these RFRs to support and guide research into areas we find interesting. If you wish, we can help to promote your work.
- Of course, anywhere you like. We can provide advice on write-ups and suitable journals or conferences.
- You (and your team) own your work and full rights to your IP. We publish these RFRs to support and guide research into areas we find interesting. If you wish, we can help to promote your work.
- Which programming language should I use?
- Since most machine learning frameworks nowadays use python, we recommend python. But if you would like to use another language, please consult us.
- Which machine learning framework could I use for these RFRs?
- If you don’t have a preference, we are encouraging standardization around TensorFlow or PyTorch. Otherwise, please consult us. We accept submissions in all frameworks Besides, we also offer a ROS-like modularization framework to encapsulate multiple frameworks.
- Can I get a (cash) reward by doing one of the RFRs?
- No, but if your research is good, we’ll tell people about it. And you may want to apply for an annual WBAI Incentive award after publishing your research.
- Should I make my code open-source?
- It is up to you, but if you would like it to be recognised in the research community, making it open-source would help, and that’s what we expect.