microsoft / microsoft/agent-learning
Introduce Structured Credit Assignment System for Episode Reinforcement Learning
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- Dominant language
- Python
- Stars
- 10
- Forks
- 9
- Avg merge
- 1d 19h
- Merged PRs (30d)
- 4
Description
Summary
Replace simple reward distribution mechanisms with structured credit assignment.
Problem
Current episode rewards may not sufficiently explain which actions contributed to success or failure.
Proposed Work
Action-level attribution
Intermediate rewards
Reward decomposition
Attribution visualization
Acceptance Criteria
Design document completed
Credit assignment model implemented
Attribution reports generated
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 locating the existing episode reward distribution, then clarify the design for action-level attribution, intermediate rewards, decomposition, and visualization. Done means the design document is complete, the credit assignment model is implemented, and attribution reports are generated.
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
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100