reward-shaping-engineering

What problem does it solve?

This Skill provides a structured framework for designing reward functions in reinforcement learning that preserve the optimal policy while accelerating training and reducing reward hacking.

Core Features & Use Cases

• Potential-based shaping that preserves policy
• Anti-hacking penalties and robust validation workflows
• Guidance on sparse vs dense rewards, normalization, and clipping
• Inverse RL considerations and distribution-shift testing

Quick Start

Implement a potential-based shaping function and combine it with the main task reward, then run validation tests across environment variants to ensure policy preservation and improved learning speed.