microsoft / microsoft/agent-learning
Implement ERL-Inspired Learning Algorithms for Small Action Policies
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 10
- Forks
- 9
- Avg merge
- 1d 19h
- Merged PRs (30d)
- 4
Description
Summary
Evaluate evolutionary and episode reinforcement learning approaches for small discrete action spaces.
Goals
Compare REINFORCE
Compare evolutionary methods
Compare hybrid methods
Deliverables
Benchmark suite
Experimental results
Recommendations
Acceptance Criteria
Research completed
Benchmark data published
Recommended algorithm documented
Contributor guide
No contributing guide indexed for this repository
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
No files, tests, or entry points are named. Start by surveying the Python repository to find the existing learning and benchmarking structure, then review the stated REINFORCE, evolutionary, and hybrid goals. Done means a benchmark suite, published experimental data, and documented algorithm recommendations meeting the acceptance criteria.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning, testing-qa
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100