reward-penalty-engineering
CommunitySystematically tune rewards for robust RL.
Education & Research#reinforcement-learning#experiment-design#robot-navigation#reward-engineering#RL-workflow#reward-library
Authormzqef
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This skill provides a process-oriented methodology for exploring, testing, and archiving reward/penalty functions in VBot navigation, enabling repeatable experimentation and knowledge retention.
Core Features & Use Cases
- Diagnose and define reward gaps by observing policy behavior and identifying concrete signals to modify.
- Hypothesize and experiment with single-variable changes, guided by defined discovery strategies, and quickly validate ideas on short training runs.
- Archive and reuse results in the reward library to prevent repeated work and share insights across projects.
Quick Start
To start, study the Exploration Cycle, reference the reward library structure, and follow the six-phase workflow (Diagnose, Hypothesize, Implement, Test, Evaluate, Archive) for iterative reward development.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: reward-penalty-engineering Download link: https://github.com/mzqef/MotrixLab/archive/main.zip#reward-penalty-engineering Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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