reward-penalty-engineering

To systematically tune rewards for robot navigation reinforcement learning, follow a structured six-phase workflow: Diagnose policy behavior, Hypothesize single-variable changes, Implement, Test on short runs, Evaluate, and Archive. This ensures repeatable experimentation and robust policy behavior.

Reward engineering in VBot navigation is the structured process of exploring, testing, and archiving reward and penalty signals. It requires analyzing the reward architecture and per-environment navigation code to diagnose gaps and iteratively modify agent behavior.

Debug ineffective reward functions by observing policy behavior to identify concrete signal gaps. Formulate a hypothesis, implement single-variable reward changes guided by defined discovery strategies, and quickly validate the adjustments on short training runs.

Yes, you can reuse reward functions across related reinforcement learning tasks by archiving validated results in a structured reward library. This prevents repeated experimentation work, retains knowledge, and shares insights across navigation projects.

To start designing penalties for VBot navigation, you need awareness of the reward architecture, the starter_kit_schedule reward library, and the per-environment navigation code. Study the Exploration Cycle to properly implement and test single-variable signal changes.