Designing a Reward Function That Actually Teaches the Agent What to Care About

In reinforcement learning (RL), the reward function defines what “success” means — it’s the feedback signal that shapes every decision the agent makes. Getting it right is surprisingly hard: if the reward is too simple, the agent plateaus; if it’s too complex, it chases noise. Reward design often feels like tuning a musical instrument that keeps changing pitch.

The Challenge

My agent had to balance two conflicting goals: maintain high detection confidence and preserve battery life on a solar-powered system. Push too hard for accuracy, and the device drains itself; play it too safe, and performance collapses. The reward function needed to capture that trade-off and adapt as sunlight and battery levels changed.

The Solution

The final version of my reward function, combines two components each step:

• r_conf: rewards detection confidence above a threshold, penalizes weak predictions.

• r_eff : penalizes excess power draw compared to available solar energy.

A context signal called energy_context, derived from battery level and solar input, scales their importance:

reward = alpha * r_conf + beta * r_eff

When energy is plentiful, confidence matters more, because alpha is large ; when it’s scarce, efficiency dominates because beta outweighs alpha. If the battery depletes entirely, the agent receives a small fixed penalty (−0.3). Enough to discourage downtime without breaking training stability.

The Takeaway

This context-aware reward function doesn’t just optimize performance; it teaches the agent when to care about each goal. Designing it was a long, iterative process, but it turned a fragile, confused learner into one that adapts intelligently to its environment.