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rl-reward-diagnosis

Analyzes reward structures in Python scripts to diagnose RL agent behavior issues.

Updated Jun 15, 2026
Search Tags:#agent behavior#diagnosis#rewards#rl#reward analysis
One-click install
npx skills add https://github.com/Piaoxuemoli/qoobeeHermes --skill rl-reward-diagnosis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: rl-reward-diagnosis
Source: https://github.com/Piaoxuemoli/qoobeeHermes/tree/main/hermes/skills/.archive/rl-reward-diagnosis
Command: npx skills add https://github.com/Piaoxuemoli/qoobeeHermes --skill rl-reward-diagnosis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

The Skill addresses the issue of diagnosing reward structure problems in Reinforcement Learning (RL) agents, which can lead to unwanted behaviors.

Core Features & Use Cases

  • Reward Structure Analysis: Analyze reward signals to diagnose agent behavior issues like random skills, wandering, or avoiding combat.
  • Use Case: If an RL agent is not performing as expected, this Skill can help identify issues within the reward function.

Quick Start

Run the skill to diagnose reward issues in your RL agent by providing the reward configuration file 'conf.py'.

Frequently Asked Questions about rl-reward-diagnosis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I diagnose RL agent behavioral issues like wandering or avoiding combat?▼

To diagnose RL agent behavioral issues, you analyze reward structures to identify root causes. By evaluating reward signals in your configuration, you can pinpoint why the agent exhibits unwanted behaviors like wandering or avoiding combat.

Why does my reinforcement learning agent perform random skills instead of expected actions?▼

Your reinforcement learning agent performs random skills due to flawed reward structures. Analyzing the reward function helps identify misaligned incentives within the configuration that cause these unexpected behavioral patterns.

How to analyze reward signals to fix performance issues in an RL workflow?▼

To analyze reward signals for fixing RL performance issues, run diagnostic scripts against your reward configuration file. This process evaluates reward structures to identify the root causes behind the agent's degraded performance.

Do I need a specific file format to diagnose reward function problems?▼

Yes, you need a Python reward configuration file named 'conf.py' to diagnose reward function problems. Providing this file allows the analysis scripts to evaluate the reward signals and identify behavioral root causes.

What is the best way to identify root causes of unwanted behaviors in reinforcement learning?▼

The best way to identify root causes of unwanted behaviors in reinforcement learning is through in-depth reward analysis. By analyzing the reward structure, you can trace the agent's actions back to misaligned reward signals.

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