munich-quantum-toolkit / munich-quantum-toolkit/predictor
✨ Support Curriculum Learning
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 87
- Forks
- 23
- Avg merge
- 6h 23m
- Merged PRs (30d)
- 35
Description
What's the problem this feature will solve?
Currently, benchmarks are randomly selected during the training process. However, starting with small and easy benchmarks and then slowly transitioning to larger and more complex ones seems to be promising and is referred to as curriculum learning in the literature.
IBM apparently also a great success with that in their AI transpiler (see https://arxiv.org/abs/2405.13196).
This could also further be applied to the action space to start with rather simple ones first and later add more complex ones.
Describe the solution you'd like
Implementation of curriculum learning in the RL part.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
The issue names no files, tests, or entry points. Start by locating the reinforcement-learning training loop and where benchmarks are randomly selected. Define how curriculum progression should work for benchmark complexity, and determine whether action-space progression is included; done means the RL training uses the requested curriculum behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100