How I Evaluated My Adaptive RL Agent

After building and training my reinforcement learning agent, the next step was defining how to evaluate whether it actually behaves well. Since the agent’s goal is to balance energy constraints with detection quality, the evaluation strategy focuses on both survival and performance, not just raw accuracy.

What “Good Performance” Means

The evaluation targets two core objectives: keeping the system alive as long as possible and maintaining useful detection confidence. To capture this, I track four key metrics:

• Survival time and battery downtime, which reflect energy efficiency

• SLA satisfaction, indicating how often the system meets minimum performance requirements

• Prediction confidence, measuring detection quality

Beyond numbers, I also look for qualitative behavior, such as whether the agent adapts its configuration sensibly as conditions change.

Evaluation Scenarios

Instead of testing in a single fixed setup, I defined six scenarios that reflect realistic and stressful operating conditions. These include summer and winter days, cloudy weather, early mornings with rising solar input, fluctuating cloud cover, and starting at night with an empty battery. Together, they test long-term endurance, seasonal effects, and the agent’s ability to dynamically scale performance when energy becomes available.

Baselines for Comparison

To understand whether the learned strategy is actually useful, I compare it against four non-learning baselines:

• A random policy as a minimal reference point

• A low-power static policy, optimized for survival

• A high-power static policy, optimized for confidence

• A mid-tier static policy, representing a fixed trade-off

These baselines define the full operational range of what is possible without adaptation, making it easier to judge when and why the RL agent performs better.

Evaluation Procedure

Each agent and baseline is evaluated across all six scenarios using both 24-hour and 48-hour simulations. For robustness, every configuration is run for 50 episodes. From these runs, I extract averaged metrics and time-series traces (such as battery level and confidence over time) to analyze both short-term reactions and long-term behavior.

Together, this evaluation setup makes it possible to assess not just how well the agent performs, but how and why it adapts under changing environmental conditions.