hyperparameter-optimization
CommunityUnified PPO hyperparam and reward-weight tuning.
Software Engineering#PPO#reinforcement-learning#hyperparameter-tuning#bayesian#AutoML#grid-search#reward-weights
Authormzqef
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill automates the joint tuning of PPO hyperparameters and reward/penalty weights, enabling a single, automated search to improve training efficiency, stability, and policy performance in robotic navigation tasks.
Core Features & Use Cases
- Unified AutoML for PPO parameters (learning rate, entropy, clipping, epochs) and reward scales
- Supports grid, random, and Bayesian search strategies with constraint-based filtering to skip unstable configurations
- Provides a ready-to-run Quick Start and analysis tooling to compare configurations and export best results
Quick Start
uv run starter_kit_schedule/scripts/automl.py --mode stage --budget-hours 12 --hp-trials 8 Get progress with uv run starter_kit_schedule/progress/automl_state.yaml
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: hyperparameter-optimization Download link: https://github.com/mzqef/MotrixLab/archive/main.zip#hyperparameter-optimization Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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