Feature/reward shaping mapper#180
Feature/reward shaping mapper#180pan-x-c merged 6 commits intoagentscope-ai:feature/data_processorfrom
Conversation
![gemini-code-assist[bot]](https://avatars.githubusercontent.com/in/956858?s=60&v=4){kind=small}
There was a problem hiding this comment.
Summary of Changes
Hello @HYLcool, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
I've implemented a new RewardShapingMapper within the trinity buffer system. This enhancement allows for the flexible modification of experience rewards by applying arithmetic operations based on specific statistical information contained within each experience. This provides a powerful mechanism to fine-tune reward signals, which is crucial for guiding reinforcement learning agents more effectively.
Highlights
- New Reward Shaping Mapper: I have introduced a new
RewardShapingMapperclass, which is anExperienceOperatordesigned to dynamically adjust the reward ofExperienceobjects based on configurable rules. - Configurable Reward Operations: The mapper supports flexible reward adjustments through
RewardShapingConfigobjects, allowing rewards to be modified using various operations (addition, multiplication, subtraction, division) based on specifiedstats_keyvalues from the experience'sinfofield and aweight. - Integration and Testing: The new mapper is seamlessly integrated into the existing
ExperienceOperatorsystem and comes with comprehensive unit tests to ensure its correct functionality and demonstrate its usage with different shaping configurations.
Using Gemini Code Assist
The full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips.
Invoking Gemini
You can request assistance from Gemini at any point in your pull request via creating an issue comment (i.e. comment on the pull request page) using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands.
| Feature | Command | Description |
|---|---|---|
| Code Review | /gemini review |
Performs a code review for the current pull request in its current state. |
| Pull Request Summary | /gemini summary |
Provides a summary of the current pull request in its current state. |
| Comment | @gemini-code-assist | Responds in comments when explicitly tagged, both in issue comments and review comments. |
| Help | /gemini help |
Displays a list of available commands. |
Customization
To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a .gemini/ folder in the base of the repository. Detailed instructions can be found here.
Limitations & Feedback
Gemini Code Assist may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments or fill out our survey to provide feedback.
You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension.
Footnotes
-
Review the Privacy Notices, Generative AI Prohibited Use Policy, Terms of Service, and learn how to configure Gemini Code Assist in GitHub here. Gemini can make mistakes, so double check it and use code with caution. ↩
|
/unittest-all |
There was a problem hiding this comment.
Code Review
This pull request introduces a RewardShapingMapper to modify experience rewards based on a set of configurations. The implementation is straightforward, but there are a few areas for improvement. I've identified a potential ZeroDivisionError that needs to be handled. I've also suggested performance and clarity improvements in the mapper implementation by replacing deepcopy with copy and refactoring a method signature. Additionally, the test coverage could be expanded to include more edge cases, and float comparisons in tests should use assertAlmostEqual for robustness. The changes to __init__.py are correct.
|
/unittest-module-buffer |
Summary
Failed Tests
Tests
Github Test Reporter by CTRF 💚 |
|
/unittest-module-buffer |
Summary
Tests
Github Test Reporter by CTRF 💚 |
2 participants
Description
Add a new mapper for reward shaping.
Checklist
Please check the following items before code is ready to be reviewed.